Quality of informal care among informal caregivers of people with dementia: A latent profile and ROC analysis

Chan Cai; Bing Cheng; Chongqing Shi; Wenli Shi; Chenyang Li; Cui Liu; Jin Sun

doi:10.1371/journal.pone.0346557

. 2026 Apr 8;21(4):e0346557. doi: 10.1371/journal.pone.0346557

Quality of informal care among informal caregivers of people with dementia: A latent profile and ROC analysis

Chan Cai ^1,^2,^‡, Bing Cheng ^1,^‡, Chongqing Shi ^1,^*, Wenli Shi ¹, Chenyang Li ¹, Cui Liu ¹, Jin Sun ³

Editor: Arupendra Mozumdar⁴

PMCID: PMC13061180 PMID: 41950281

Abstract

Background

The quality of informal care for people with dementia (PwD) has gained increasing importance, as most PwD prefer home-based care over institutional placement. However, evidence-based intervention programs tailored to distinct care quality profiles remain limited. Additionally, the absence of clear thresholds to identify PwD receiving low-quality informal care poses a challenge for research and clinical practice. Thus, this study aimed to identify the profiles of quality of care (QoC) among informal caregivers of PwD, explore influencing factors of different profile, and determine the optimal cut-off score of the Exemplary Care Scale (ECS).

Methods

A cross-sectional survey was conducted. A total of 213 dyads of PwD and their informal caregivers were recruited from memory clinic, rehabilitation clinic, and neurological clinic of a tertiary hospitals and communities in Wuhan, Hubei, China, between July 15, 2023, and July 14, 2024. Latent profile analysis (LPA) was employed to identify QoC profiles. Multinomial logistic regression was performed to explore influencing factors of profile membership. Receiver Operating Characteristic (ROC) analysis was conducted to determine the ECS cut-off score.

Results

Three distinct QoC profiles were identified: high (24.41%), moderate (44.60%), and low (30.99%). Among informal caregivers, lower monthly income, insufficient social support, and higher perceived overload were associated with low QoC profile, whereas, better quality of pre-illness relationship with PwD and greater activities of daily living (ADL) of PwD were associated with high QoC. ROC analysis yielded an optimal ECS cut‑off score of 15, with high sensitivity (0.993) and specificity (0.955).

Conclusions

This study identified three distinct QoC profiles among caregivers of PwD, underscoring the heterogeneity of informal care quality. The identified predictors and the validated ECS cut‑off score of 15 provide an empirical basis for developing tailored screening tools and targeted interventions for high‑risk caregiver subgroups.

1. Introduction

Dementia is a syndrome characterized by progressive cognitive impairment that is associated with marked declines in patient's ability to perform activities of daily living (ADL), learning, work, and social interactions [1]. Globally, the number of people with dementia (PwD) is projected to reach 152 million by 2050 [2]. Currently, one in four PwD reside in China, which ranks first worldwide in terms of number of cases [3]. Most PwD live at home and are cared for by informal caregivers (CGs), typically spouses or children [4]. This care is based both on the cultural tradition of caring for elders and on a greater knowledge of their preferences and values than formal caregivers may have or provide. However, sustained caregiving often generates considerable stress and burden, along with a range of physical and mental health issues for caregivers [5], which can in turn undermine the quality of care (QoC) provided [6,7]. In this sense, some studies have shown that low QoC adversely affects the health of PwD, increasing the risk of recurrent hospitalizations and institutionalization, outcomes that can ultimately affect their life expectancy [8,9]. Thus, early identification of caregivers at risk of providing low-quality care and the implementation of targeted support are urgently needed.

QoC is a multidimensional construct encompassing the assessment of key caregiving aspects. These include behaviors that may harm care recipients’ (CRs) well-being, the adequacy of caregiving behaviors in meeting the CRs’ basic needs, and responsiveness to CRs’ psychological needs for respect and enjoyable social engagement [10]. Traditionally, QoC assessment has focused on identifying harmful caregiver behaviors and assessing the extent to which CRs’ basic and instrumental daily needs are met [10]. However, these dimensions are not sensitive to the feelings and wishes of CRs and, therefore, fail to reflect all the aspects that quality care for PwD living in the community should consider. To address this gap, Dooley et al. conducted an extensive literature review and proposed the concept of exemplary care [11]. Exemplary care extends beyond meeting basic needs to include respecting CRs’ feelings, preferences, opinions, and values, while actively avoiding actions that criticize or undermine their dignity. It is fundamentally characterized by sensitivity and respect demonstrated throughout the caregiving process. This approach conveys to CRs the message that they are loved, respected, and worthy of special attention, helping to maintain their dignity and integrity, and holistically reflecting the concept of person-centered care.

In this line, and based on the conceptual framework of exemplary care, Dooley and colleagues developed the Exemplary Care Scale (ECS) to evaluate the performance of informal caregivers in home-based dementia care [11]. The scale comprises two core domains: care provision and respect demonstration. Compared to other instruments commonly used to assess informal care quality, such as the comprehensive but lengthy QUALCARE Scale [12], the functionally-oriented Family Caregiving Consequences Inventory (FCCI) [13], and the negatively-focused Potential Harmful Behaviors scale (PHB) [14,15], the ECS stands out with its unique positive perspective on caregiving. Moreover, its cross-cultural validity has been confirmed through rigorous validation studies in diverse populations, including the United States [16], Argentina [17], and China [18]. These attributes make the ECS a particularly reliable and practical tool for assessing QoC for PwD in Chinese household settings.

Although much research focuses on the total ECS score to assess overall quality of care and the factors that influence it [16–20], this variable-centered approach fails to capture individual differences and does not allow for the observation of heterogeneity across subgroups. A shift to a person-centered approach is therefore needed to identify distinct caregiver profiles for personalized support. Although such person-centered methods have identified meaningful profiles in elderly care [21], their application specifically to dementia caregivers remains scarce. This gap limits the empirical basis for providing targeted support to high-risk caregivers who provide low QoC for PwD.

On the other hand, although heterogeneity analysis effectively captures differences among groups [22], its utility for rapid clinical screening is limited. To address this practical challenge, we propose to investigate in greater depth through this study the optimal cut-off score of ECS using Receiver Operating Characteristic (ROC) analysis. This approach would allow clinicians and community professionals to quickly identify high-risk groups and establish action strategies, using only the total score as a reference. The proposed simplification of the scoring process represents a methodological innovation that preserves the advantage of the LPA in identifying heterogeneity and improves its clinical applicability, while also providing empirical evidence for understanding the causes of this heterogeneity.

In this context, our hypotheses are: (1) the quality of informal care could be categorized into different latent profiles according to the ECS; (2) the quality of informal care provided by caregivers varies depending on individual characteristics, stressors, quality of life, and social support. (3) On this basis, and through ROC analysis, it may be possible to establish an optimal cutoff score with high sensitivity and specificity that allows for the identification of different levels of care quality and, specifically, the identification of low-quality care.

These results would have two practical implications. First, they would provide a scientific basis for developing targeted interventions tailored to different subgroups. Second, they could significantly improve the assessment of care quality and, thereby, efficiency.

To respond to these hypotheses, we set four objectives: (1) to identify potential subgroups of the quality of care among informal caregivers of PwD,; (2) to clarify the factors associated with these subgroups; (3) to identify categorization thresholds for the ECS that can be applied in clinical and community practice; and (4) to propose rapid screening criteria that enable health and social organizations to quickly identify high-risk individuals.

This study was guided by McClendon's Extended Stress Process Model [16]. The model takes “QoC” as the core outcome variable, shifting the research perspective from “caregiver-centered” to the “caregiver-patient dyadic integration.” Including multidimensional influences provides a systematic theoretical framework for understanding QoC in dementia. The results of this study could provide important empirical evidence for the subsequent development of precise intervention strategies for different subgroups of caregivers.

2. Materials and methods

2.1 Design and participants

This cross-sectional study was conducted between July 15, 2023, and July 14, 2024, in Wuhan, Hubei Province, China. Using convenience sampling, we recruited PwD and their informal caregivers attending memory clinic, rehabilitation clinic, and neurological clinic in the hospital and communities. PwD were eligible if they: (1) were aged ≥60 years;(2) met the WHO International Classification of Diseases, 10th Revision (ICD-10) diagnostic criteria for dementia [23]; and (3) had lived at home with family for at least three months prior to enrollment.

All caregivers were primary informal caregivers of PwD who were unpaid and met the following conditions: (1) aged ≥18 years, (2) providing at least four hours per day on caregiving for no less than three months, thereby fulfilling the role of the primary caregiver [24]; (3) often accompanying patients to see a doctor, the best understanding of the patient's condition, and basic living conditions; (4) provide informed consent; and (5) were able to read, comprehend the Chinese questionnaire, and communicate effectively in Mandarin (the official language of China). Caregivers were excluded if they had experienced other major stressful life events (e.g., bereavement or divorce) within the past three months. All participants were assessed by researchers to ensure they understood the study.

2.2 Data collection

Data were collected using paper-based questionnaires administered during face-to-face interviews with caregivers. Caregivers served as the sole respondents, providing information on both their own status and that of the PwD. After providing written informed consent, participants completed the questionnaires anonymously. Participants with reading difficulties completed the questionnaires with the help of trained research staff. The questionnaires were completed and collected on-site. After collecting questionnaires, the investigators immediately checked the questionnaires for missing or obvious logical errors, and any issues were resolved on the spot. Each questionnaire was completed within 15–25 minutes.

2.3 Sample size calculation

An a priori sample size calculation was performed using G*Power 3.1.9.7. With a two-tailed, a medium effect size (f² = 0.30), a power of 0.95, a statistical level α = 0.05 [25], and 20 predictors, the minimum required sample size was 120. To ensure robust model fit, stable parameter estimation, and adequate power for subgroup comparisons in LPA, we targeted a minimum of 200 participants [26], consistent with methodological recommendations for person-centered approaches. Initially, 248 caregiver-PwD dyads were recruited. After excluding 35 dyads due to incomplete responses or other reasons, 213 valid dyads were included in the final analysis.

2.4 Ethics approval

The study was approved by the Ethics Committee of TianYou Hospital Affiliated to Wuhan University of Science &Technology (LL-2023-07-14-002).

2.5 Measures

All information was obtained from informal caregivers, covering two primary domains. The first domain comprised sociodemographic and clinical details of PwD, including age, gender, education level (1= ≤ 6 years; 2 = 7–9 years; 3= ≥ 10 years), type of dementia, years since diagnosis, number of children, ADL, and residence. The second domain encompassed caregivers’ own characteristics: age, gender, education level, relationship with PwD (1 = children; 2 = partner; 3 = other), cohabitation status (1 = yes; 2 = no), length of caregiving, monthly income (RMB) (1= ≤ 2000; 2 = 2000–5000; 3= ≥ 5000), and physical health. Physical health was assessed with the single-item question, “How do you rate your general health?”, rated on a 5-point scale from very poor to very good. For analysis, responses were dichotomized as impaired (1 = very poor, poor, fair) and good (2 = good, very good). Additionally, caregivers completed five instruments assessing quality of care, perceived overload, quality of the pre-illness relationship with PwD, depressive symptoms, and social support.

2.5.1 Activities of daily living (ADL).

The Barthel Index (BI), developed by Florence Mahoney and Dorothy Barthel [27], is a 10-item scale used to assess patients’ ADL. Total scores range from 0 to 100, with higher scores indicating greater independence. Based on the BI score, patients’ ADL is classified into four levels: independence (100), mild dependence (65–95), moderate dependence (40–60), and severe dependence (0–35) [28]. Data were collected via face-to-face interviews conducted by trained researchers with family caregivers.

2.5.2 Quality of care (QoC).

QoC was measured using the Exemplary Care Scale (ECS), which was translated into Chinese by Lau et al [18]. The ECS was completed by the informal caregivers. It consists of 11 items that fall into two dimensions: Provide (items 1–5) and Respect (items 6–11). Each item is rated on a 4-point scale (0 = never, 1 = sometimes, 2 = often, and 3 = always). The total score ranges from 0 to 33. Higher scores indicate better QoC. Cronbach’s alpha for the entire scale was 0.816.

2.5.3 Quality of pre-illness relationship.

The Relationship Rewards Scale (RRS) was used to assess the quality of the pre-illness relationship between caregiver and CR [15]. Caregivers assessed the frequency (1 = never, 2 = sometimes, 3 = often, 4 = always) at which they perceived pre-illness interpersonal relationships with CRs as rewarding, with higher scores indicating better quality of pre-illness relationship. The RRS was translated into Chinese using Brislin’s guidelines [29]. Cronbach’s alpha in this sample was 0.840.

2.5.4 Perceived overload.

Caregiver perceived overload was assessed using the 4‑item Overload Scale. Items were rated on a 4-point scale (1 = not at all, 2 = somewhat, 3 = quite a bit, 4 = completely), with total scores ranging from 4 to 16, with higher scores indicating a higher perceived overload [30]. The Chinese version was developed following Brislin's guidelines [29]. Cronbach’s alpha of the Overload scale was 0.791.

2.5.5 Depression.

Caregivers’ depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) developed by Radloff [31]. The 20-item CES-D scale asks respondents how often they had experienced depressive symptoms in the past week. A cut-off score of ≥16 was used to indicate clinically significant depression [32]. Cronbach’s alpha for the CES-D was 0.877 in this study.

2.5.6 Social support.

Social support was evaluated using the 10-item Social Support Rating Scale (SSRS), which was designed for the Chinese population by Xiao, based on the Social Support Questionnaire (SSQ) and Interview Schedule for Social Interaction (ISSI) [33,34]. The SSRS comprises three dimensions: subjective support, objective support, and support utilization. Total scores range from 12 to 66 [35], and are categorized into three levels: low-level (≤22), medium-level (23–44), and high-level (≥45). In this study, Cronbach's alpha for the SSRS was 0.804.

2.6 Statistics analysis

We conducted four statistical analyses. First, descriptive statistics were used to summarize participant characteristics. Second, LPA was performed using Mplus 8.3 to identify distinct QoC profiles, with each item of the ECS serving as an indicator. Model fit was evaluated using multiple indices: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC), Lo-Mendell-Rubin likelihood ratio test (LMR), Bootstrapped likelihood ratio test (BLRT), and Entropy [36]. Lower AIC, BIC, and aBIC values indicated better fit. Differences between latent profile models were compared using the LMR and BLRT. Significant LMR and BLRT P-value suggested that a k class model fit significantly better than a k-1 class model. Entropy ≥0.8 indicates a classification accuracy exceeding 90%. Cohen's d was calculated to further validate the accuracy of the classification (0.2–0.5: small; 0.5–0.8: medium; > 0.8: large) [37,38]. Third, to examine the effects of predictor variables on latent profiles of QoC, we employed the R3STEP command in Mplus, which effectively controls for potential effects arising from classification errors [39]. Fourth, ROC analysis was performed to determine the optimal cut-off value for the ECS using SAS 9.4. The optimal threshold was defined as the value that maximized Youden’s index (sensitivity + specificity–1), with higher values indicating better discriminatory performance [40]. The area under the curve (AUC) was computed to evaluate the accuracy of identifying low QoC. AUC values were interpreted as follows: ≥ 0.90 (excellent), 0.80–0.89 (good), 0.70–0.79 (fair), and <0.70 (poor). The P-value <0.05 indicated statistical significance.

3. Results

3.1 Sample characteristics

A total of 213 dyads of PwD and their informal caregivers were recruited. The mean age of PwD was 77.29 ± 9.42 years, and most were cared for by spouses or children. Notably, 54.9% of the PwD were male. Caregivers were predominantly female (64.8%) and 75.6% lived with PwD. Further details are presented in Table 1. Additionally, Table 2 summarizes the scores of key caregiver variables, including quality of pre-illness relationship, perceived overload, and social support, which were used in subsequent analyses.

Table 1. General characteristics of PwD and caregivers (N = 213).

PwD	M ± SD or N (Percentage)	Informal caregivers	M ± SD or N (Percentage)
Age (years)	77.29 ± 9.42	Age(years)	59.06 ± 14.33
Gender		Gender
Male	117 (54.9)	Male	75 (35.2)
Female	96 (45.1)	Female	138 (64.8)
Education level		Education level
≤6 years	112 (52.6)	≤6 years	45 (21.1)
7-9 years	87 (40.8)	7-9 years	94 (44.1)
≥10 years	14 (6.6)	≥10 years	74 (34.8)
Type of dementia		Relationship with PwD
Alzheimer	128 (60.1)	Spouse	76 (35.7)
Vascular dementia	71 (33.3)	Children	110 (51.6)
Other	14 (6.6)	Other^a	27 (12.7)
Years since diagnosis		Cohabitation status
~1	58 (27.2)	Yes	161 (75.6)
~5	99 (46.5)	No	52 (24.4)
≥5	56 (26.3)	Length of care (year)
Numbers of children		~1	70 (32.9)
0	1 (0.5)	~5	97 (45.5)
1	50 (23.5)	≥5	46 (21.6)
2	70 (32.8)	Monthly income (RMB)
≥3	92 (43.2)	≤2000	62 (29.1)
ADL		2000 ~ 5000	67 (31.5)
Severe	101 (47.4)	≥5000	84 (39.4)
Moderate	36 (16.9)	Physical health
Mild	59 (27.7)	Poor	140 (65.7)
No	17 (8.0)	Good	73 (34.3)
Residence		Depression
Rural	70 (32.9)	No	144 (67.6)
Urban and town	143 (67.1)	Yes	69 (32.4)

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Note: PwD, people with dementia; ADL, activities of daily living; Other^a, included siblings, friends, and other relatives; M, mean; SD, standard deviation; RMB, Ren Min Bi (the Chinese yuan, ¥).

Table 2. Scores for caregivers’ quality of pre-illness relationship, perceived overload, and social support (N = 213).

Variables	M ± SD
Quality of pre-illness relationship	11.68 ± 2.82
Perceived overload	9.63 ± 2.90
Social support	39.25 ± 7.04

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Note: M, mean; SD, standard deviation.

3.2 Latent profile analysis

Fit indices for the 1- to 5-profile solutions are shown in Table 3. The AIC, BIC, and aBIC values decreased continuously as the number of profiles increased. The 4-profile model was excluded because the LMR P-value was not significant (P > 0.05). For the 2-, 3-, and 5-profile models, both LMR and BLRT P-values were significant, indicating that each k profiles provided a significantly better fit than k-1 profiles model. Although the 5-profile model yielded lower AIC, BIC, and aBIC values and higher entropy than the 3-profile model, the additional profile contributed limited substantive information and resulted in smaller, less interpretable subgroups. Considering both statistical fit and parsimony, the 3-profile model was selected as the optimal model. The average probability of QoC belonging to the profile ranged from 92.0% to 95.2% (Table 4), indicating excellent classification accuracy. All Cohen’s d values for pairwise comparisons between profiles exceeded 0.80 (Table 5), further supporting the distinctiveness of the identified subgroups.

Table 3. Fit information of the latent profile models.

profile	Likelihood	AIC	BIC	aBIC	LMR(P)	BLRT(P)	Entropy	Proportion
1	−2596.885	5237.769	5311.718	5242.006	–	–	–	–
2	−2370.772	4809.544	4923.828	4816.092	0.000	0.000	0.892	0.338/0.662
3	−2289.612	4671.224	4825.844	4680.083	0.016	0.000	0.855	0.244/0.446/0.310
4	−2250.044	4616.088	4811.043	4627.258	0.517	0.000	0.851	0.300/0.174/0.235/0.291
5	−2017.855	4283.771	4519.061	4297.251	0.017	0.000	0.956	0.146/0.150/0.113/0.221/0.371

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Note: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion; LMR, Lo-Mendell-Rubin likelihood ratio test; BLRT, Bootstrapped likelihood ratio test.

Table 4. Probability of different categories in the latent profile analysis.

Class	Profile 1	Profile 2	Profile 3
Profile 1	0.952	0.000	0.048
Profile 2	0.000	0.920	0.080
Profile 3	0.028	0.046	0.927

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Table 5. Means, standard deviations, and Cohen’s d for the three profiles of Exemplary Care Scale.

	M(SD)	N(%)	Score Ranges	Cohen's d
Low QoC	11.35 (2.10)	66 (30.99%)	[5, 16]	d_2-1 = 3.24
Moderate QoC	18.13 (2.09)	95 (44.60%)	[12, 22]	d_3-2 = 2.06
High QoC	22.73 (2.51)	52 (24.41%)	[19, 29]	d_3-1 = 4.97

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Note: QoC, quality of care; M, mean; SD, standard deviation; N(%): Number (Percentage). Cohen's d_2–1: the standardized mean difference between moderate QoC group and low QoC group; Cohen's d_3–1: the standardized mean difference between high QoC group and low QoC group; Cohen's d_3–2: the standardized mean difference between high QoC group and moderate QoC group.

The three latent profiles exhibited distinct scoring patterns across the eleven items of ECS. Profile 1 had the highest mean scores on all items and was therefore labeled the high QoC profile, comprising 24.4% of the sample. Profile 2 showed moderate scores on all items and was designated the moderate QoC profile, accounting for 44.6%. Profile 3 had the lowest scores on each item and was termed the low QoC profile, representing 31.0% of the caregivers. The scores of the three profiles on the eleven item of QoC are depicted in Fig 1.

3.3 Influencing factors analysis

After univariate analyses identified significant variables (S1 Table), multinomial logistic regression using the R3STEP procedure in Mplus was conducted to examine their effects on profile membership, with the moderate QoC profile as the reference. Results are presented in Table 6.

Table 6. Results of multinomial logistic regressions predicting profile membership (R3STEP) (N = 213).

Variable	low VS moderate (ref)		high VS moderate (ref)
Variable	β (SE)	OR (95% CI)	β (SE)	OR (95% CI)
background variables
Monthly income (RMB)	−1.359* (0.667)	0.257(−2.666, −0.053)	−0.221 (0.481)	0.802 (−1.164, 0.722)
Quality of pre-illnes relationship with PwD	−0.235 (0.133)	0.790(−0.496, 0.025)	0.316** (0.119)	1.372 (0.082, 0.550)
stressors
PwD’s ADL	0.090 (0.358)	1.094(−0.612, 0.791)	1.070** (0.309)	2.914 (0.464, 1.675)
Caregivers’ perceived overload	0.359** (0.134)	1.431(0.095, 0.622)	0.066 (0.136)	1.068 (−0.202, 0.333)
caregiver resource
Social support	−0.206** (0.065)	0.814(−0.333, −0.079)	0.070 (0.056)	1.072 (−0.040, 0.179)

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Note: PwD, people with dementia; ADL, activity of daily living; OR, Odds Ratio; 95%CI, 95% Confidence Interval; ref, reference; RMB, Ren Min Bi (the Chinese yuan, ¥); SE, Standard Error; *P < 0.05; **P < 0.01; ***P < 0.001.

Lower monthly income (β = −1.359, P < 0.05), insufficient social support (β = −0.206, P < 0.01), and higher perceived overload (β = 0.359, P < 0.01) were significantly associated with low QoC profile. In contrast, better quality of pre-illness relationship (β = 0.316, P < 0.01) and and better ADL function of the PwD (β = 1.070, P < 0.001) were associated with high QoC profile.

3.4 ROC analysis

To validate the clinical applicability of the ECS cut-off, the sample was randomly split into two independent subsamples (approximately 50%/50%). In the derivation subsample, ROC analysis identified an optimal ECS cut-off score of 15, yielding a high sensitivity of 0.993, specificity of 0.955, and an AUC of 0.998 (95% CI [0.993, 0.999], P < 0.001; Fig 2; Table 7). The mean difference between the two groups was statistically significant with a large effect size (Table 8), further supporting this cut-off point. In the validation subsample, the identical cut-off of 15 was confirmed, achieving a Youden’s index of 0.928, sensitivity of 0.985, specificity of 0.943, and an AUC of 0.990 (95% CI [0.975, 0.999], P < 0.001; S2 Table; S1 Fig). The consistent performance across both subsamples supports the robustness and generalizability of the ECS cut-off score of 15 for identifying low QoC.

Table 7. Criterion values and coordinates of ROC Curve (derivation subsample).

Cut-off point	Sensitivity	Specificity	Youden’s index
14	1.000	0.839	0.839
15	1.000	0.968	0.968
16	0.962	0.968	0.930
17	0.911	1.000	0.911

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Note: Estimates in italics are the suggested optimal cut-off points; ROC: Receiver Operating Characteristic.

Table 8. Negative and positive groups classified by the optimal cut-off score of the Exemplary Care Scale.

	M(SD)	N(%)	Score Ranges	Cohen's d
Negative group	11.10 (1.96)	31 (28.18%)	[5,15]	d = 3.49
Positive group	19.85 (2.69)	79 (71.82%)	[12,29]

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Note: M, Mean; SD, Standard Deviation; N(%): Number (Percentage). Cohen's d: the standardized mean difference between the Negative group and the Positive group;

Discussion

This study identified three distinct profiles of informal care quality among caregivers of PwD, confirming significant heterogeneity in the quality of informal care. The low QoC profile scored the lowest on items related to emotional aspects (e.g., Hobby, Gathering), reflecting a pattern of survival-oriented, passive care that addressed only basic life needs. Social engagement, emotional support, and respectful treatment were largely absent, indicating minimal attention to the PwD’s dignity, preferences, or psychosocial well-being [18]. The low QoC profile was driven by a convergence of caregiver adversities: economic strain, high overload, and inadequate support, highlighting structural determinants of poor outcomes. This profile may be particularly common among caregivers of severely dependent PwD, those with prolonged care duration, or those struggling with limited financial resources to sustain care. The moderate QoC profile, which represented the largest caregivers group, demonstrated adequate performance in daily life care tasks but exhibited insufficient emotional support. While basic life needs were reasonably well met, caregivers showed limited responsiveness to the PwD’s psychological needs, individual interests, or social participation. This task‑centered approach left considerable room for improvement in overall care quality. In contrast, the high QoC profile exemplified dignity-oriented care. Caregivers not only delivered high quality life support but also actively consider the PwD’s emotional needs, autonomy, and social engagement. This profile fully embodied the core principles of comprehensive, high-quality care provision.

These distinct profiles were underpinned by different configurations of background variables, stressors, and resources. Overall, our findings are largely consistent with the Extended Stress Process Model [16], confirming that background variables, primary stressors, and caregiver resources significantly predict the quality of informal care, but did not find a similar impact from caregivers’ quality of life. Specifically, contrary to both the model and prior research [7,16,41], caregiver depression, a core dimension of quality of life, did not show a significant association with QoC. This nonsignificant association can be attributed to Chinese cultural norms, particularly filial piety and family responsibility. This cultural value has been linked to reduced caregiver burden and enhanced QoC [42], as home-based dementia care in China is rooted in familism and filial norms [43]. Within this context, family caregivers may find fulfillment in their caregiving role [44], reinforced by community recognition, which enables them to sustain caregiving standards despite experiencing depressive symptoms. This mechanism explains our core finding: although depressive symptoms differed significantly across the three latent profile groups (S1 Table), they did not predict QoC. In other words, depression effectively captured caregiver heterogeneity, yet its impact on caregiving behavior was buffered by cultural responsibility. Thus, the observed nonsignificant association reflects the unique cultural context of family caregiving in China, rather than any inadequacy in scale validity. Future research should develop culturally sensitive models for assessing QoC.

Monthly income was a key factor for QoC [45,46]. Higher-income caregivers can provide more material support during treatment and rehabilitation, contributing to better QoC and patient outcomes. Low education and rural residence were strong predictors of low income [47], thereby indirectly increasing the risk of poor QoC. These findings identify caregivers with low income, low education, and rural residence as a multiple disadvantaged subgroup, underscoring the need for integrated economic and educational support from social services.

In alignment with previous studies [15,48], a positive pre-illness relationship serves as a protective factor for QoC. As Steadman et al. demonstrated [49], caregivers reporting higher premorbid relationship satisfaction showed significantly less stress and less reactivity to memory and behavioral problems, as well as better problem-solving skills and more effective communication. Conversely, caregivers with poor pre-illness relationships may perceive caregiving responsibilities as an additional burden, leading to negative attitudes and behaviors [46], ultimately compromising the quality of informal care. This finding emphasizes that pre-illness relationship quality, while not modifiable, is a critical factor. It suggests prioritizing interventions for caregivers with poorer pre-existing relationships, as they may require more intensive support to achieve exemplary care.

At the same time, when PwD have a greater ability to perform basic activities of daily living, this situation correlated positively with the quality of care, while caregiver burden showed a negative correlation, results that have also been observed in a large number of studies [7,45,50]. Caregivers of PwD experience high levels of stress and overload in long-term care [51], compounded by competing family and occupational responsibilities. Full-time, long-term care impairs caregivers’ mental health and care capacity, further diminishing quality of care [41,52].

In addition to the caregiver-related factors, patient-level characteristic also play a critical role in shaping QoC. Research has shown that cognitive impairment or psychiatric symptoms are prevalent in most patients at the time of diagnosis [3], substantially compromising their daily functioning and necessitating caregiver assistance. However, many caregivers of PwD struggle to provide consistent and attentive care due to insufficient care capacity and a lack of care resources [53,54], ultimately resulting in diminished QoC. This challenge is compounded by the fact that progressive disability is often accompanied by aphasia and comprehension deficits [3], making it difficult for patients to express their needs accurately. These challenges can undermine caregivers’ patience and lead to inadequate care [55]. Consequently, as PwD’s ADL decline, QoC may also decrease. Therefore, assessing the ability of PwD to perform ADL is an indispensable tool for providing individualized care and for proposing measures to increase these abilities, all with the goal of improving the quality of care for PwD.

The buffering model of social support posits that social support buffers against the adverse impact of stressful events [56]. However, China’s current caregiving support system is underdeveloped [57]; caregivers predominantly rely on informal family networks, while formal support at both community and national levels remains insufficient [58]. The current unidimensional and incomplete support structure underscores the urgent need to strengthen community- and national-level formal support, thereby building a diversified social support network that can provide caregivers with adequate resources and assistance.

Combining LPA and ROC analysis, the study revealed that a cut-off point of 15 had the highest sensitivity and specificity for identifying individuals with low quality of informal care, offering an evidence‑based threshold for future research and practice. This cut‑off score enables healthcare professionals to rapidly identify caregivers at high risk of providing inadequate care, prompting timely evaluation and follow‑up. For social services planning, this benchmark supports the efficient allocation of resources by prioritizing interventions for the most vulnerable caregiver-PwD dyads. Public health professionals are suggested to collect information on the quality of informal care for PwD, implement corresponding interventions for family caregivers of PwD whose scores are less than 15, and provide necessary support.

5. Limitations

First, due to the cross-sectional design, causal relationships cannot be established. Future longitudinal studies are needed to strengthen the conclusions. Second, the sample was restricted to informal caregivers of PwD recruited from a single city in China, which may limit the generalizability of the findings. Future research should expand the sample to caregivers from diverse regions and urbanization levels to enhance external validity. Third, although the ECS cut-off score of 15 demonstrated high sensitivity and specificity in the ROC analysis, this threshold was derived from a single sample and requires cross-validation in different samples to support its clinical applicability. Fourth, the use of self-reported ECS may be subject to social desirability bias and recall errors. Future studies could incorporate objective measures (e.g., clinical assessments, or behavioral observations) to improve data reliability. Fifth, our sample included both co-residing and non-co-residing caregivers, which may entail distinct caregiving contexts and challenges. Stratifying by residential status in future research would enable a more nuanced understanding of the unique experiences and support needs of each subgroup.

6. Conclusion

This study confirmed all three hypotheses. Using LPA, we identified three distinct QoC profiles: low, moderate, and high, demonstrating significant heterogeneity among caregivers of PwD. Guided by McClendon’s Extended Stress Process Model, we further found that lower monthly income, inadequate social support, and higher perceived overload significantly predicted low QoC; better pre-illness relationship quality and greater ability to perform ADL of PwD were key predictors of high QoC. These findings underscore the need for public health professionals to comprehensively assess these multidimensional factors. Targeted interventions should be applied simultaneously to heterogeneous informal caregivers to improve care quality and prevent its deterioration. To facilitate early identification, we established an optimal ECS cut-off score of 15 via ROC analysis, which demonstrated excellent sensitivity and specificity. This evidence-based threshold provides a practical screening tool for timely detection and targeted intervention in both clinical and community settings.

Supporting information

S1 Table. Univariate analysis of different latent profile (N = 213).

(PDF)

pone.0346557.s001.pdf^{(110.3KB, pdf)}

S2 Table. Criterion values and coordinates of ROC Curve (validation subsample).

(PDF)

pone.0346557.s002.pdf^{(54.8KB, pdf)}

S1 Fig. ROC of the Exemplary Care Scale score for classifying low and high QoC.

(PDF)

pone.0346557.s003.pdf^{(65.7KB, pdf)}

S1 File. Data.

(XLSX)

pone.0346557.s004.xlsx^{(131KB, xlsx)}

Acknowledgments

We are grateful to all the people who agreed to participate in this study for their support, time, and patience. We also thank all staff in the hospital and community centers for their patience and cooperation.

Abbreviations

QoC: quality of care
PwD: people with dementia
ECS: Exemplary Care Scale
RRS: Relationship Rewards Scale
BI: The Barthel Index
CES-D: Epidemiologic Studies Depression Scale
SSRS: Social Support Rating Scale
LPA: Latent profile analysis
ADL: activity of daily living
CRs: care recipients
CGs: caregivers
ref: reference
CI: confidence interval

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0346557.r001

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Reviewer #1: Thank you for the opportunity to review this manuscript. However, the manuscript would benefit from clearer framing of the research gap, improved methodological clarity, and deeper interpretation of results. My comments below aim to support the authors in enhancing the rigor and clarity of the study.

1. “However, few studies have examined heterogeneity within groups of caregivers of PwD or the characteristics of quality of care, resulting in a lack of empirical basis for early identification and precise support for high-risk caregivers. Notably, with the rapidly growing population of PwD in China, informal caregivers are experiencing an increasingly heavy burden of care.”

While this statement attempts to establish a research gap, it is vague and lacks specificity and supporting evidence. I recommend reframing the gap more precisely, ideally by identifying exactly what has been understudied and how the current study addresses that gap.

2. The statistical analysis section (2.3) demonstrates an appropriate selection of methods, particularly in applying LPA and utilizing relevant model fit criteria. However, the writing suffers from unclear variable definitions, grammatical issues, and imprecise explanations of key statistical concepts. I suggest restructuring the paragraph for clarity, clearly defining all abbreviations, and correcting misleading interpretations (e.g., regarding entropy). These revisions would enhance the section’s clarity and improve its support for the study analytical rigor and reproducibility.

3. The discussion section largely restates that there are three QoC groups, but it does not provide a deep interpretation of what distinguishes these profiles, how meaningful the differences are, or how the findings relate to the theoretical framework.

4. The discussion does not explain why specific predictors (e.g., ADL, pre-illness relationship) influence care quality profiles, nor does it contextualize the findings by comparing them to prior research. A more critical engagement with existing literature would strengthen the discussion and improve the paper contribution to the field.

Reviewer #2: 1. It is unclear which approach ultimately led to the final target of >210 participants, even though the sample size calculation section specifies requirements for both LPA (≥150 participants) and multinomial logistic regression (>200 participants). Could you please clarify if the final target was selected based on the higher of the two estimates or if there was another way that you arrived at the number 213?

2. The sample size calculation assumes 3 latent profiles, but this assumption is not supported by previous research or pilot results. Giving a reference will assure readers of its validity, since the assumed number of profiles directly affects the sample size needed for LPA.

3. It's unclear what is meant by the statement "the mean scores of 11 items of the ECS were used as observed variables." Although it sounds like you averaged the items before performing the analysis, the individual item scores are typically used as the input in latent profile analysis. It appears from Figure 1 that you used the 11-item scores for each individual in the analysis, and then you computed the average scores for each profile to describe them. If this was not what you thought, kindly reword it.

4. The ECS cut-off of 15 is determined by the ROC analysis, and this shall be validated in a different sample, which would improve its clinical applicability across different populations, even though the reported sensitivity and specificity are high.

**********

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Reviewer #2: No

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PLoS One. 2026 Apr 8;21(4):e0346557. doi: 10.1371/journal.pone.0346557.r002

Author response to Decision Letter 1

29 Sep 2025

Response to Editor:

1.I agree with one of the reviewers, that more details are necessary on how authors calculated the sample size. Kindly provide all assumptions for sample size estimation for future research.

Revised

Thanks very much for your review of our manuscript. In response, we have supplemented and refined the description of the sample size estimation accordingly.

For multinomial logistic regression, the sample size should be at least 5-10 times the number of independent variables [24]. With 20 variables included in this study and accounting for a potential 20% rate of invalid questionnaires, a minimum of 120 participants was initially required. To enhance the accuracy and validation efficacy of the potential category model, a sample size of at least 200 was deemed necessary [25]. Initially, 248 participants were recruited. However, 35 were excluded due to incomplete responses or other reasons, resulting in 213 valid participants included in the final analysis (85.89% response rate). (lines 165-173)

[24] Xu, Y., Chen, Z., Tang, X. et al. Latent profile analysis of nutrition knowledge, attitudes, and practices and their influencing factors in maintenance hemodialysis patients. Sci Rep 15, 17246 (2025). https://doi.org/10.1038/s41598-025-02142-4.

[25] Chen X, Wang Z, Wang S, et al. Latent profiles of ambivalence over emotional expression in young breast cancer patients: A cross-sectional study. Int J Nurs Sci. 2025;12(4):379-385. Published 2025 Jun 16. doi:10.1016/j.ijnss.2025.06.005.

2. The manuscript need careful proofreading. Kindly revise the manuscript carefully for sentence formatting, internal consistency, and punctuation.

Revised. We have carefully checked and revised the manuscript to ensure that the sentence format, internal consistency and punctuation are correct.

Response to Reviewer 1:

Indeedly, while this statement attempts to establish a research gap, it is vague and lacks specificity and supporting evidence. Thank you for your valuable comments. We have revised clearly specify the under-investigated areas within current research and to explicitly elaborate on how our study is designed to address these specific gaps.

Many researches mainly focus on the total score of ECS scale to assess overall QoC and its influencing factors [16-20]. However, these studies adopt a variable-oriented approach, which fails to capture individual differences and obscures heterogeneity among different subgroups. It is essential to identify distinct patterns of the QoC within the group and to implement targeted interventions based on their different characteristics. Although existing research has identified distinct caregiver profiles associated with different types of low QoC in the elderly [21], few studies have examined characteristic within groups of caregivers of PwD, resulting in a lack of empirical basis for precise support for high-risk caregivers of PwD supplied low QoC. (lines 95-104)

[21] Yan E, Lai DWL, Sun R, et al. Typology of family caregivers of older persons: A latent profile analysis using elder mistreatment risk and protective factors. J Elder Abuse Negl. 2023;35(1):34-64. doi:10.1080/08946566.2023.2197269

Revised.

Thank you for your careful checks. We have made a comprehensive revision to the presentation of the statistical analysis section to ensure clarity and accuracy.

Each item score on the ECS serves as explicit indicators to establish a LPA model using Mplus 8.3. The most suitable model was selected based on fit indices [35], including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC), Lo-Mendell-Rubin likelihood ratio test (LMR), Bootstrapped likelihood ratio test (BLRT), and Entropy. Lower values of AIC, BIC, and aBIC indicated a superior model fit. Differences between latent profile models were compared using the LMR and BLRT. A significant P-value suggests that the model with k classes fits the data better than the model with k-1 classes. Entropy was used to assess classification accuracy, with values closer to 1 indicating more precise classification; an entropy value ≥0.8 indicates a classification accuracy exceeding 90%. Cohen's d was calculated to further validate the accuracy of the classification (0.2-0.5: small; 0.5-0.8: medium; >0.8: large) [36,37]. (lines 230-242)

Revised.

Thank you very much for this insightful and critical comment. We have thoroughly revised the Discussion section to address these points as follows, analyzing the differences and significance of the three groups of characteristics, as well as linking the results of this study to the theoretical framework.

We used LPA to categorize the caregivers of PwD into three distinct profiles, i.e., high QoC (24.41%), moderate QoC (44.60%), and low QoC (30.99%). Statistically significant differences were found between the high and moderate QoC profile in terms of pre-illness relationship quality and PwD’ ADL. Logistic regression analysis revealed that the high QoC profile was linked to great quality of pre-illness relationship and superior PwD’ ADL. Similarly, significant differences in economic status, perceived overload, and social support were observed between the low and moderate QoC profile. The low QoC profile was associated with low economic affordability, high perceived overload, and inadequate social support. These results highlight that tailored strategies based on the distinct characteristics of each caregiver subgroup. Priority should be given to caregivers facing financial strain, high perceived overload, and insufficient social support. (lines 328-339)

The Extended Stress Process Model hints that background variables, primary stressors, quality of life, and caregiver resources collectively influence the quality of informal care. Our study verified that background variables, primary stressors, and caregiver resources impacted the quality of informal care, except caregivers’ quality of life. Specifically, affordability of living expenses, pre-illness relationship quality, the ADL of PwD, perceived overload, and social support decided three QoC profiles. These findings provide an innovative perspective on the importance of economic status, relationship quality, caregiving stress, social support in mitigating the deterioration of QoC among caregivers of PwD. (lines 340-348)

Revised.

Thanks for your valuable feedback. We have revised to provide a more thorough explanation of how specific predictors (such as Activities of Daily Living and pre-illness relationship quality) influence care quality profiles, and have contextualized our findings through systematic comparisons with previous studies.

Align with previous studies [15,42], a positive pre-illness relationship serves as a protective factor for QoC. As Steadman et al. demonstrated [43], caregivers reporting higher premorbid relationship satisfaction showed significantly less stress and less reactivity to memory and behavioral problems, and better problem solving skills and more effective communication. Conversely, caregivers with poor pre-illness relationships may perceive caregiving responsibilities as an additional burden, leading to negative attitudes and behaviors [41,43], ultimately compromising the quality of informal care. The finding emphasizes the critical role of caregiving context during care, suggesting that interventions aimed at improving QoC should consider pre-illness relationship quality. (lines 352-362)

The decline in ADL among PwD significantly increases caregiver overload [48]. Research has shown that cognitive impairment or psychiatric symptoms are prevalent in most patients at the time of diagnosis [3], substantially compromising their daily functioning and necessitating caregiver assistance. However, many caregivers of PwD struggle to provide consistent and attentive care due to insufficient care capacity and a lack of care resources [49], ultimately resulting in diminished QoC. Furthermore, disability is often accompanied by aphasia and comprehension deficits, making it difficult for patients to express their needs accurately. These challenges may undermine caregivers’ patience and lead to inadequate care. (lines 369-378)

[43] Steadman PL, Tremont G, Davis JD. Premorbid relationship satisfaction and caregiver burden in dementia caregivers. J Geriatr Psychiatry Neurol. 2007;20(2):115-119. doi:10.1177/0891988706298624

[48] Kim, B., Noh, G.O. & Kim, K. Behavioural and psychological symptoms of dementia in patients with Alzheimer’s disease and family caregiver burden: a path analysis. BMC Geriatr 21, 160 (2021). https://doi.org/10.1186/s12877-021-02109-w

[49] Report on the family survival status of Alzheimer's disease patients in China. Alzheimer’s Disease Chinese, 2020-1. 2019. URL: http://weixin.moreedge.cn/bps_2019/index.php

Response to Reviewer 2:

1.It is unclear which approach ultimately led to the final target of >210 participants, even though the sample size calculation section specifies requirements for both LPA (≥150 participants) and multinomial logistic regression (>200 participants). Could you please clarify if the final target was selected based on the higher of the two estimates or if there was another way that you arrived at the number 213?

In accordance with the Editor's suggestion, the revisions have been incorporated. Please see Response to Editor Answer No.1 (lines 165-173 in the revised manuscript).

2.The sample size calculation assumes 3 latent profiles, but this assumption is not supported by previous research or pilot results. Giving a reference will assure readers of its validity, since the assumed number of profiles directly affects the sample size needed for LPA.

Revised.

Thanks the reviewer for this important comment. The assumption regarding the number of potential profiles in the initial submission was not sufficiently grounded. In response, we have removed the unsupported statement and revised.

To enhance the accuracy and validation efficacy of the potential category model, a sample size of at least 200 was deemed necessary [25]. (lines 169-170)

3.It's unclear what is meant by the statement "the mean scores of 11 items of the ECS were used as observed variables." Although it sounds like you averaged the items before performing the analysis, the individual item scores are typically used as the input in latent profile analysis. It appears from Figure 1 that you used the 11-item scores for each individual in the analysis, and then you computed the average scores for each profile to describe them. If this was not what you thought, kindly reword it.

Revised.

We have corrected the incorrect statement in the manuscript.

Each item score on the ECS serves as explicit indicators to establish a LPA model using Mplus 8.3. (lines 230-231)

Revised.

The original expression of the third limitation has been revised to improve its clarity and precision.

Third, although this study reported high sensitivity and specificity, the ECS cut-off value of 15 was determined through ROC analysis. Future validation in different samples are needed to enhance the clinical applicability of this threshold. (lines 419-421)

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PLoS One. doi: 10.1371/journal.pone.0346557.r003

Decision Letter 1

Arupendra Mozumdar

15 Dec 2025

Dear Dr. Shi,

Please submit your revised manuscript by Jan 29 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Arupendra Mozumdar, Ph.D., M.Sc.

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Additional Editor Comments:

I went through the rebuttals from the authors and the reviewers' comments on the second version of the manuscript. The manuscript is composed of scores of grammatical errors, awkward sentence structure, and confusing statements. Tow of the reviewers did painstaking yet remarkable jobs to identify the shortcomings and provided valuable comments to improve the manuscript. Keeping the importance of the topic and the level of efforts from the authors in mind I would like to give another chance to the manuscript. Me too have some suggestions to improve the manuscript. I am requesting authors to revise and resubmit the manuscript for another round by addressing all the comments form the reviewers.

The manuscript is probably drafted and written by someone whose first language is not English. So I will eagerly request authors copy-editing the manuscript by someone who is proficient in academic English before submission. My specific comments are given below.

Please provide marginal effect plots or at least a table of mean values along with 95%-CI for independent variables for each of the three groups in the final model. This will provide information to interpret the magnitude and nature of the relationship between predictors and latent profile membership. Instead of just seeing if a variable is a significant predictor, these plots will show how much the probability of profile membership shifts as the independent variable changes.

Reviewer from the last round of review asked to validate ECS cut-off of 15 in a different sample to improve its clinical applicability. I suggest dividing the sample into two sub-samples, say 50-50% or 60-40%, using a random number generator. Then use one sub-sample to ROC analysis get a cut-off, apply the cut-off to other sub-sample, and calculate the sensitivity and specificity.

Line 55-56: Please mention the geographic reference for the expected number of 152 million PwD in 2050. Will the number of 152 million reach worldwide, in Asia, or only in Chaina?

Line 69: Please revise as “…that may negatively impact care recipients’ (CRs) well-being, …”

Line 70: What does it mean by adequacy of caregiving behaviors? The meaning is unclear to me. Kindly reconstruct the sentence.

Line 72-73: Rephrase from “…CRs’ daily basic and instrumental daily needs…” to “…CRs’ basic and instrumental daily needs…”

Line 104: Change the word ‘supplied’. ‘Provided’ would be a better choice of words here.

Line 126: Please clarify whether traditional total score analysis assumes “the same score can represent different qualities” or LPA assumes "the same…”. If the answer is the latter, please rephrase and bring the last clause in the middle of the sentence.

Line 138: Statement #3 is not a hypothesis. To me it is the application of the learnings from hypothesis testing from the first two hypotheses.

Line 98-102: Do authors mean ‘subgroups’ when they use the word group here? I guess authors wanted to make the justification for LPA here, but the communication is not clear and suddenly the introduction of the concept of group confuses the readers.

Line 160-162: The sentence could be rephrased as "Participants with reading difficulties completed the questionnaires with the help of trained research staff.”

Line 167-170: Thank you for addressing this point raised in the previous round of review. The justifications for the sample size are given for analytical sample. However, readers would like to know the representativeness of the sample along with statistical assumptions like expected value of response distribution, level of confidence, margin of error, power, etc.

Figure 1: Please add legends for all three lines. Technically this should not be a line diagram because the variables in the x-axis are completely independent of each other not showing any trend. It would be better if authors leave the dots and remove the lines connecting the data points. Also, I would recommend adding ‘whiskers’, denoting 95%-CIs of item scores, to each dot. This way authors can show how or if the distributions of scores are overlapped or not. But adding whiskers is not mandatory, if that makes the figure more cluttery.

Table 5: Remove the row titled ROC if anything else is not provided.

Line 321. Revise “…life, and caregiver resources predicted…” into “…life, and caregiver resources; and predicted…”

Line 337-338: Authors would need to add a consequence or a finding to this sentence, for example, "...highlight that tailored strategies need to be developed based on the distinct characteristics of each caregiver subgroup.”

Line 343: Kindly reconstruct the sentence. I guess, authors want to say here that the caregivers' quality of life was the sole factor among all independent variable examined that did not show a statistically significant effect on the quality of care provided. One version could be “The study confirmed that background variables, primary stressors, and caregiver resources influenced the quality of informal care, but did not find a similar impact from caregivers’ quality of life.”

Line 348. These findings provide an innovative perspective on the importance of economic status, relationship quality, caregiving stress, and social support in mitigating the deterioration of QoC among caregivers of PwD. Meaning of the sentence is unclear. Kindly rephrase. Do authors mean low QoC by using the word ‘deterioration’? What is ‘innovative’ perspective on importance of such variables, and how this innovation could ‘mitigate’ the deterioration of QoC (or low QoC)?

Table-5 Kindly properly format the table of multinomial regression. The names of the variables are presented as it is entered into the software. Please rewrite the names of the variables in a meaningful way.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

Reviewer #6: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

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3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: I Don't Know

Reviewer #4: I Don't Know

Reviewer #5: Yes

Reviewer #6: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: (No Response)

Reviewer #6: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

**********

Reviewer #3: Quality of informal care among informal caregivers of patients with

dementia: a latent profile and ROC analysis

Authors are commended for their superb study focusing on quality of informal care of a vulnerable patient population. Findings not only highlight the profile of the informal caregiver but also, perhaps unintentionally, provides a sense of the predicament of the care recipient.

General comments

The reviewer suggested revision of sentence structures. However, authors are encouraged to thoroughly engage with the manuscript to ensure that all grammatical issues are addressed. Alternatively, authors might consider consulting with a language and/or copy editor.

Abstract

Line 26- 27: the sentence “Additionally, the absence of clearly thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice” should either read “Additionally, the absence of clear thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice” or alternative, “Additionally, the absence of clearly demarcated [or specified] thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice”

Line 27: it is assumed that authors are referring to PwD needs in “… thresholds for identifying PwD with low quality of informal care [needs]?” Or are they referring to identifying PwD receiving “low quality of informal care” or being delivered to?

Line 28: the word “of” seems misplaced in the sentence structure “… aimed to identify potential profiles of among informal caregivers of PwD…”. Furthermore, it is not clear what “potential profiles” authors are referring to? Similarly, it is not clear what authors are referring to with regards to “profile” / “profiles” in the Methods section [i.e line 36]. Furthermore, in the Results section authors refer to quality of care profiles [line 38-39]? It is only after reading the rest of the manuscript that it became clear what “profiles” refer to. This should be remedied.

Line 29: do authors have one profile in mind stating “… explore influencing factors of different profile, …” or should “profile” be revised to read “profiles”?

Line 39-41: it is not clear whether authors are referring to informal carer’s or PwD’s “worse affordability of living expenses, insufficient social support and higher perceived overloaded”?

Line 45: it is not “obvious” which “classification characteristics” authors are referring to.

In general, the abstract does not seem to have been written with the same robustness as the body of the manuscript.

Introduction

Line 99: it might be helpful to clarify which group authors are referring to stating “within the group” for the reader. It is assumed authors are referring to QoC among informal caregivers?

Line 104-115: it is not clear why authors consider reference to LPA, “an individual-centered statistical analysis”, as background versus information related to Materials and Methods? In the event that authors deem reference to LPA necessary in the Introduction section they are encouraged to rephrase LPA-related sentences to meet typical background-related standards. Ultimately, authors need to separate research methodology from practical application / implementation of their findings and move applicable methodology sentences / detail to these sections of the manuscript.

Line 124-130: ultimately reference to statistical analyses applied [(1) … “using LPA …” (2) “… through logistic regression analysis, …” and (3) “… using ROC analysis …”] reflect authors’ choice of statistical analysis with regard to their study and has no bearing on the objectives of the study. Would authors agree that the same objectives could have been addressed using different statistical approaches?

Line 138-139: the reviewer is not convinced that (3) qualifies as a “hypothesis” [line 135]? The sentence rather seems to address “2.3 Statistical analysis”?

Materials and Methods

Line 145: the sentence structure needs to be addressed. It could possibly read “… caregivers attending memory clinics, rehabilitation clinics, and neurological clinics …” or “… caregivers attending either a memory clinic, rehabilitation clinic, and neurological clinic …”.

Line 149: is there a reason why authors reference the ICD-10 [1993] versus the ICD-11 [2019]?

Line 154: what does “clear consciousness” entail? How was “normal intelligence” defined? What criteria did authors apply [and/or how did they gauge] to determine inclusion of informal caregivers based on “clear consciousness, normal intelligence, good communication skills”?

Line 157-174: authors include among these sentences detail regarding a) data collection b) sample size calculation c) ethics approval. It is suggested that authors provide applicable sub-headings to distinguish between various Materials and Method aspects. Streamlining of sub-headings and sentence structures will enhance their paper.

Line 157: it is not clear whether “paper questionnaires” and “independent questionnaires” refer to the ESC caregivers completed or were different questionnaires completed? It might be worth mentioning how many questionnaires [name / label these] participants were expected to complete [possibly in table format] especially given the extended number of questionnaires caregivers were expected to complete [as referred to in 2.2.2]. Such clarity will also assist to grasp the extensive carers profile authors obtained? Did both PwD and the caregivers complete questionnaires independently. Who provided basic information of PwD [line 187]?

Line 160-162: please address the sentence structure as it is not clear what authors intend to convey.

Line 162: does “The questionnaires were distributed and collected immediately” imply that “Participants were expected to complete questionnaires immediately upon distribution.”

Line 167 and 170: while authors reference studies which applied LPA [and indicate sample size calculation [including references] for their respective studies] the reviewer is not convinced that these studies could / should be referenced to corroborate authors’ statements regarding their sample size calculations. Authors are encouraged to preferably source, and reference, (pure) statistics-related publications addressing calculations of sample size (applicable to authors’ study).

Line 180: does Lau, et al. have a reference?

Line 180: the sentence structure should read “ESC was completed ….”

Line 191: the sentence structure should read "Relationship Rewards Scale (RRS) was used to …”. Furthermore, a) the reviewer was unable to source a “Relationship Rewards Scale” and b) use of reference 15 should perhaps be reconsidered as a reference providing the reader to access to RRS.

Results

Line 259: a) “Caregivers was predominantly were female …” should read “Caregivers were predominantly female …” b) please refrain from starting a sentence with a number / percentage. Line 259 could possibly read “Caregivers were predominantly female (64.8%) and 75.6% of the caregivers lived with PwD.”

Table 1: a) it is suggested that authors consider providing a separate table with results based on the different questionnaires caregivers completed. It is debatable whether affordability of living expenses; physical health, depression, quality of pre-illness relationship, perceived overload and social support could / should be considered as “Characteristics” of caregivers. In 2.2.2. authors refer to these as “independent variables” [as reported in line 296-300] b) consider providing a footnote indicating what “Other” relationships with PwD were.

Line 264: revise “… caregivers of PwD were shown …” to read “… caregivers of PwD are shown …”

Table 4: “LPAa” is not explained as a footnote to Table 4.

Figure 1: the only legend regarding colours used corresponds to blue “Low QoC”.

Table 5: the heading of the table indicates the test performed rather than what is reported in Table 5 as a result of the test performed.

Table 7: what is the intention of “ROC” in the table [given the table heading]?

Discussion

Line 319: “Based on Extended Stress Model, …” should read “Based on the Extended Stress Model, …”

Line 321: “… caregiver resources predicted …” should read “… caregiver resources predict ...”

Line 328-336: seems these lines repeat what authors already stated in the Result section and could be better utilized to elicit discussion.

Line 337-338: this sentence is incomplete.

Line 342: provide reference corroborating the statement.

Line 343: it is not clear what authors imply with “… except caregivers’ quality of life”? It seems authors emphasize that the caregiver’s quality of care is impacted but not the person’s quality of life per se?

Line 345: consider revising the word “decided”. It seems authors are stating that the three QoC profiles identified during the study pivoted on / revolved around / was determined by the list of aspects mentioned in the sentence?

Line 381: it is assumed that “… consisting other studies …” should read “… consistent with other studies …”? Irrespective, the sentence does not make sense.

Line 409-411: this sentence seem more fitting as a conclusion instead of a discussion point.

Reviewer #4: Thank you for your paper some comments to consider

People with dementia rather than patients should be used

Background-Whilst this is well written it seems too long and would benefit from some editing

Line 71 pleasant activities ?

Capital letter mid sentence line 101

Lines 360-362 some grammatical issues

Generally just check grammar throughout

Reviewer #5: Good morning.

First, I would like to congratulate the authors on their excellent work and on giving me the opportunity to review it. The topic addressed, the quality of care, is relevant, and the authors' approach provides information that may be useful to the scientific community. The effort made by the authors to convey their results is evident.

As a result of my review, I have the following suggestions regarding your manuscript. My suggestions are divided into general suggestions, which affect the manuscript, and specific suggestions, which are described by line number and section of the manuscript to facilitate their location.

I hope to have the opportunity to review your manuscript again with the suggested corrections.

Reviewer #6: The comments are attached to the document. They are clear and direct and the author can be able to action the issues raised.

**********

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #3: No

Reviewer #4: No

Reviewer #5: Yes: Vicente Martín MorenoVicente Martín MorenoVicente Martín MorenoVicente Martín Moreno

Reviewer #6: Yes: Moses MutuaMoses MutuaMoses MutuaMoses Mutua

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PLoS One. 2026 Apr 8;21(4):e0346557. doi: 10.1371/journal.pone.0346557.r004

Author response to Decision Letter 2

21 Feb 2026

Response to Reviewers

Dear Editor and Reviewers,

Thank you for your detailed and constructive comments on my manuscript. According to the editor and reviewers’ comments, we have made extensive corrections to our previous manuscript. Two versions of the revised manuscript are submitted: one with no markings and another with the changes highlighted in yellow for clarity. All authors have reviewed and approved the response letter and the revised manuscript. Below is the point-by-point responses to the editor and reviewers, the red font is the revised original text in the manuscript:

Response to Editor:

1.I went through the rebuttals from the authors and the reviewers' comments on the second version of the manuscript. The manuscript is composed of scores of grammatical errors, awkward sentence structure, and confusing statements. Tow of the reviewers did painstaking yet remarkable jobs to identify the shortcomings and provided valuable comments to improve the manuscript. Keeping the importance of the topic and the level of efforts from the authors in mind I would like to give another chance to the manuscript. Me too have some suggestions to improve the manuscript. I am requesting authors to revise and resubmit the manuscript for another round by addressing all the comments form the reviewers. The manuscript is probably drafted and written by someone whose first language is not English. So I will eagerly request authors copy-editing the manuscript by someone who is proficient in academic English before submission.

Thank you for the decision and feedback. We will have the manuscript professionally copy-edited and fully address all reviewer comments in the revision.

2.Please provide marginal effect plots or at least a table of mean values along with 95%-CI for independent variables for each of the three groups in the final model. This will provide information to interpret the magnitude and nature of the relationship between predictors and latent profile membership. Instead of just seeing if a variable is a significant predictor, these plots will show how much the probability of profile membership shifts as the independent variable changes.

Revised.

Thanks very much for your review of our manuscript. Revised as suggested. We have added the OR and 95% CI for independent variables in Table 6. (line 313)

3.Reviewer from the last round of review asked to validate ECS cut-off of 15 in a different sample to improve its clinical applicability. I suggest dividing the sample into two sub-samples, say 50-50% or 60-40%, using a random number generator. Then use one sub-sample to ROC analysis get a cut-off, apply the cut-off to other sub-sample, and calculate the sensitivity and specificity.

Revised.

Thank you for this important methodological suggestion. We have followed the recommended procedure: randomly splitting the sample (50-50%), deriving the cut-off in one subsample, and validating it in the other. The analysis robustly confirmed the ECS cut-off of 15 in both independent subsamples, supporting its reliability. The detailed results have been added to the manuscript.

To validate the clinical applicability of the ECS cut-off, the sample was randomly split into two independent subsamples (approximately 50%/50%). In the derivation subsample, ROC analysis identified an optimal ECS cut-off score of 15, yielding a high sensitivity of 0.993, specificity of 0.955, and an AUC of 0.998 (95% CI [0.993, 0.999], P < 0.001; Fig. 2; Table 7). The mean difference between the two groups was statistically significant with a large effect size (Table 8), further supporting this cut-off point. In the validation subsample, the identical cut-off of 15 was confirmed, achieving a Youden’s index of 0.928, sensitivity of 0.985, specificity of 0.943, and an AUC of 0.990 (95% CI [0.975, 0.999], P < 0.001). The consistent performance across both subsamples supports the robustness and generalizability of the ECS cut-off score of 15 for identifying low QoC. (line 316-326)

4.Line 55-56: Please mention the geographic reference for the expected number of 152 million PwD in 2050. Will the number of 152 million reach worldwide, in Asia, or only in China?

Revised.

Thank you for your valuable comments. We have revised clearly specify the data "152 million in 2050" refers to the global level.

Globally, the number of people with dementia (PwD) is projected to reach 152 million by 2050 [2]. (line 55-56)

5.Line 69: Please revise as "…that may negatively impact care recipients’ (CRs) well-being, …"

Revised. (line 70)

6.Line 70: What does it mean by adequacy of caregiving behaviors? The meaning is unclear to me. Kindly reconstruct the sentence.

Revised.

Thank you for pointing out this issue. In the revised manuscript, we explicitly define "adequacy of caregiving behaviors" as "the adequacy of caregiving behaviors in meeting the CRs’ basic needs". (line 71)

7.Line 72-73: Rephrase from "…CRs’ daily basic and instrumental daily needs…" to "…CRs’ basic and instrumental daily needs…"

Revised. (line 74-75)

8.Line 104: Change the word ‘supplied’. ‘Provided’ would be a better choice of words here.

Revised. Thank you for your suggestion. I have revised ‘supplied’ to ‘provided’ and further refined the sentence for clarity and academic tone. The revised sentence now reads:

This gap limits the empirical basis for providing targeted support to high-risk caregivers who provide low QoC for PwD. (line 104-105)

9.Line 126: Please clarify whether traditional total score analysis assumes "the same score can represent different qualities" or LPA assumes "the same…". If the answer is the latter, please rephrase and bring the last clause in the middle of the sentence.

Thank you for this correct observation. We agree that the phrase was ambiguously phrased and that the limitation belongs to the traditional score approach. As the specific sentence has been revised following thet suggestion of Reviewer 3 (to state objectives without specifying methods). Please see Response to Reviewer 3 Answer No.10 (line 127-132 in the revised manuscript).

10.Line 138: Statement #3 is not a hypothesis. To me it is the application of the learnings from hypothesis testing from the first two hypotheses.

Revised.

Thank you for your precise feedback. We agree that the original statement described an application. Following Reviewer 6’s suggestion, we have revised hypothesis: (3) On this basis, and through ROC analysis, it may be possible to establish an optimal cutoff value with high sensitivity and specificity that allows for the identification of different levels of care quality and, specifically, the identification of low-quality care. (line119-122)

11.Line 98-102: Do authors mean ‘subgroups’ when they use the word group here? I guess authors wanted to make the justification for LPA here, but the communication is not clear and suddenly the introduction of the concept of group confuses the readers.

Thank you for this clarification. We have revised the paragraph to address the ambiguity. The text now clearly distinguishes the limitations of the traditional variable-centered approach (focusing on total scores) from the need for a person-centered approach (to identify distinct profiles).

A shift to a person-centered approach is therefore needed to identify distinct caregiver profiles for personalized support. Although such person-centered methods have identified meaningful profiles in elderly care [21], their application specifically to dementia caregivers remains scarce. (line101-104)

12.Line 160-162: The sentence could be rephrased as "Participants with reading difficulties completed the questionnaires with the help of trained research staff."

Revised. (line 165-166)

13.Line 167-170: Thank you for addressing this point raised in the previous round of review. The justifications for the sample size are given for analytical sample. However, readers would like to know the representativeness of the sample along with statistical assumptions like expected value of response distribution, level of confidence, margin of error, power, etc.

Revised.

Thank you for the valuable comments.We have conducted an a priori power analysis using G*Power and revised the manuscript accordingly, clarifying both the calculation basis and the methodological considerations for determining the sample size.

An a priori sample size calculation was performed using G*Power 3.1.9.7. With a two-tailed, a medium effect size (f2=0.30), a power of 0.95, a statistical level α=0.05 [25], and 20 predictors, the minimum required sample size was 120. To ensure robust model fit, stable parameter estimation, and adequate power for subgroup comparisons in LPA, we targeted a minimum of 200 participants [26], consistent with methodological recommendations for person-centered approaches. (line171-176)

14.Figure 1: Please add legends for all three lines. Technically this should not be a line diagram because the variables in the x-axis are completely independent of each other not showing any trend. It would be better if authors leave the dots and remove the lines connecting the data points. Also, I would recommend adding ‘whiskers’, denoting 95%-CIs of item scores, to each dot. This way authors can show how or if the distributions of scores are overlapped or not. But adding whiskers is not mandatory, if that makes the figure more cluttery.

Revised.

We add legends for all three lines. We leave the dots and remove the lines connecting the data points and adding ‘whiskers’. (line 299)

15.Table 5: Remove the row titled ROC if anything else is not provided.

Thank you for your comment. We have checked Table 5 (now table 6) carefully and found no row titled "ROC" in the current version. The table includes only the variables listed in the submitted manuscript. Could you please clarify which "ROC" you are referring to? We are happy to make the necessary changes once we understand your request.

16.Line 321. Revise "…life, and caregiver resources predicted…" into "…life, and caregiver resources; and predicted…"

Thank you for your suggestion. I agree with the proposed revision. However, in response to Reviewer 6’s comment on content redundancy, the sentence cited at Line 321 has been removed during revision. The relevant section no longer appears in the manuscript.

17.Line 337-338: Authors would need to add a consequence or a finding to this sentence, for example, "...highlight that tailored strategies need to be developed based on the distinct characteristics of each caregiver subgroup."

Revised. Thank you for your suggestion on Lines 337-338. I agree that a clearer implication is needed. In response to Reviewer 6’s comments, I have rewritten this section.

18.Line 343: Kindly reconstruct the sentence. I guess, authors want to say here that the caregivers' quality of life was the sole factor among all independent variable examined that did not show a statistically significant effect on the quality of care provided. One version could be "The study confirmed that background variables, primary stressors, and caregiver resources influenced the quality of informal care, but did not find a similar impact from caregivers’ quality of life."

Revised.

Overall, our findings are largely consistent with Extended Stress Process Model [16], confirming that background variables, primary stressors, and caregiver resources significantly predict the quality of informal care,but did not find a similar impact from caregivers’ quality of life. (line 357-360)

19.Line 348. These findings provide an innovative perspective on the importance of economic status, relationship quality, caregiving stress, and social support in mitigating the deterioration of QoC among caregivers of PwD. Meaning of the sentence is unclear. Kindly rephrase. Do authors mean low QoC by using the word ‘deterioration’? What is ‘innovative’ perspective on importance of such variables, and how this innovation could ‘mitigate’ the deterioration of QoC (or low QoC)?

Thank you for your comment on Line 348. I agree the original sentence was unclear. In response to Reviewer 6’s comment that the Discussion should not present new results, I have rewritten this section.The relevant section no longer appears in the manuscript.

20.Table-5 Kindly properly format the table of multinomial regression. The names of the variables are presented as it is entered into the software. Please rewrite the names of the variables in a meaningful way.

Thank you for your suggestion. I have revised Table 5 (now Table 6) and rewritten all variable names to ensure they are presented clearly and meaningfully. (line 313)

Response to Reviewer 3:

General comments:

Revised. We have carefully checked and revised revision of sentence structures, thoroughly engage with the manuscript to ensure that all grammatical issues are addressed.

一．Abstract

1.Line 26-27: the sentence "Additionally, the absence of clearly thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice" should either read "Additionally, the absence of clear thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice" or alternative, "Additionally, the absence of clearly demarcated [or specified] thresholds for identifying PwD with low quality of informal care poses a challenge for research and clinical practice"

Revised.

Additionally, the absence of clear thresholds to identify PwD receiving low-quality informal care poses a challenge for research and clinical practice. (line 25-27)

2.Line 27: it is assumed that authors are referring to PwD needs in "… thresholds for identifying PwD with low quality of informal care [needs]?" Or are they referring to identifying PwD receiving "low quality of informal care" or being delivered to?

Revised. Thank you for the feedback. We have revised the sentence to eliminate the ambiguity.

Additionally, the absence of clear thresholds to identify PwD receiving low-quality informal care poses a challenge for research and clinical practice. (line 25-27)

3.Line 28: the word "of" seems misplaced in the sentence structure "… aimed to identify potential profiles of among informal caregivers of PwD…". Furthermore, it is not clear what "potential profiles" authors are referring to? Similarly, it is not clear what authors are referring to with regards to "profile" / "profiles" in the Methods section [i.e line 36]. Furthermore, in the Results section authors refer to quality of care profiles [line 38-39]? It is only after reading the rest of the manuscript that it became clear what "profiles" refer to. This should be remedied.

Revised.

Thank you for this essential critique regarding the unclear introduction of a core concept. We have remedied this by providing a clear and full definition at the first occurrence of the term “profiles” in the Introduction (Line 28), specifying that they refer to “profiles of quality of care (QoC)”. Subsequently, in the Methods and Results sections, we consistently use this defined term (“QoC profiles”) to ensure clarity and avoid ambiguity.

Thus, this study aimed to identify the profiles of quality of care (QoC) among informal caregivers of PwD, explore influencing factors of different profile, and determine the optimal cut-off value of the Exemplary Care Scale (ECS). (line 27-29)

4.Line 29: do authors have one profile in mind stating "… explore influencing factors of different profile, …" or should "profile" be revised to read "profiles"?

Revised. The term of "profile" was revised to "profiles".

5.Line 39-41: it is not clear whether authors are referring to informal carer’s or PwD’s "worse affordability of living e

Attachment

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pone.0346557.s010.docx^{(142.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0346557.r005

Decision Letter 2

Arupendra Mozumdar

3 Mar 2026

Quality of informal care among informal caregivers of people with dementia : a latent profile and ROC analysis

PLOS One

Dear Dr. Shi,

Please submit your revised manuscript by Apr 17 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Arupendra Mozumdar, Ph.D., M.Sc.

Academic Editor

PLOS One

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Thank you very much for submitting the revised manuscript titled “Quality of informal care among informal caregivers of people with dementia : a latent profile and ROC analysis” after incorporating the suggestions from the editor and the reviewers. After carefully going through the revised manuscript and the responses to reviewers’ comments, I still have some concerns, addressing those, I believe, would enhance the quality of the manuscript up to the standard of publication for the journal.

I would like to request the authors to strictly follow the instructions for authors while preparing tables and figures, and how the in-text citation to those tables and figures have been made. Many of the tables misses the key elements in titles, footnotes, and legends. We would not be able to accept a manuscript that which has not been prepared strictly following the instructions to authors. You may consider of taking help from the experts for preparing the manuscript.

Some tables in the appendices seem redundant. The table numbers in the manuscript and the appendices are overlapping, titles and notes are incomplete, and without any reference to the text of the manuscript. Some column headings are given in complete lower case. Requesting authors to be careful while preparing the manuscript even if those are supporting materials. Appendices are not meant for dumping of information and analyses, but to provide readers the context without confusing them.

Method: Sample size calculation. Please remove the response rate from the paragraph. I believe all 248 pair of caregivers and PwD responded to the study, but the current analyses included data from 213 out of 248 pairs. This is not the response rate as we experience in survey where not all persons are approached may not respond.

Fig 2. Please mention the name of the score / scale, of which the cut-off of 15 was estimated, in the title of ROC curve.

Appendix-2 title of the ROC curve. Font not in English. No title of the figure. The ROC curve looks completely different what is provided in the manuscript (Fig 2). What is the full form of AUC.

Table 8. Please include the name of the variable in the title of the table (like you did for Table 5) of which the mean and SD have been calculated.

Specifically, contrary to both the model and prior research [7,16,41], caregiver depression, a core dimension of quality of life, did not show a significant association with QoC.

Further, while explaining such non-significant association between depression and QoC author questioned the validity of the scale of depression in Chinese population, and inferred that the depression may be under-reported and that underreporting could be due to fear of social disapproval or strong internalized expectations among Chinese culture.

At this point there are two situations. Either, the depression scale is not valid for the Chinese population, and we should not use the variable at all, or may be the non-significant result is the significance of the association. May be the caregivers are providing good quality of care to the PwD, as it is the cultural practice to care about them, in spite of themselves feeling depressed in providing care due to different socioeconomic constraints.

Adding to my surprise, Table 1 of Appendix 1 showed a highly significant association of depression (p< 0.001) across three latent profile groups. Further, the manuscript also reported that “Notably, lower income was also significantly associated with higher depressive symptoms, suggesting that financial strain undermines psychological well-being and further compounds care quality risks. These findings therefore identify caregivers with low income, low education, rural residence, and elevated depressive symptoms as a multiple disadvantaged subgroup, underscoring the need for integrated economic, mental health, and educational support from social services.”

So, I am getting confused what is the actual finding of the paper related to QoC and depression.

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PLoS One. 2026 Apr 8;21(4):e0346557. doi: 10.1371/journal.pone.0346557.r006

Author response to Decision Letter 3

18 Mar 2026

Response to Editor

Dear Editor,

Thank you for your detailed and constructive comments on my manuscript. According to the editor and reviewers’ comments, we have made extensive corrections to our previous manuscript. Two versions of the revised manuscript are submitted: one with no markings and another with the changes highlighted in yellow for clarity. Below are our point-by-point responses to the editor, the red font is the revised original text in the manuscript:

Journal Requirements:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Thank you for this important reminder. We have carefully reviewed the entire reference list to ensure its completeness and accuracy. All in-text citations have been verified against the reference list to confirm that no citations are missing and that all listed references are cited in the text. We have consulted PubMed to confirm that none of the cited papers have been retracted. All necessary corrections have been made in the revised manuscript, and we believe the reference list now meets the journal's standards.

Additional Editor Comments:

1. Thank you very much for submitting the revised manuscript titled “Quality of informal care among informal caregivers of people with dementia : a latent profile and ROC analysis” after incorporating the suggestions from the editor and the reviewers. After carefully going through the revised manuscript and the responses to reviewers’ comments, I still have some concerns, addressing those, I believe, would enhance the quality of the manuscript up to the standard of publication for the journal.

Thank you very much for the opportunity to further revise our manuscript. We have carefully addressed all the remaining concerns raised by you and the reviewers. The point-by-point responses are provided below, and all revisions are clearly marked in the manuscript. We believe the revised manuscript is now suitable for publication and look forward to your final decision.

2. I would like to request the authors to strictly follow the instructions for authors while preparing tables and figures, and how the in-text citation to those tables and figures have been made. Many of the tables misses the key elements in titles, footnotes, and legends. We would not be able to accept a manuscript that which has not been prepared strictly following the instructions to authors. You may consider of taking help from the experts for preparing the manuscript.

Thank you very much for your valuable feedback. We sincerely apologize for the formatting issues in our tables and figures. We have now carefully reviewed the journal’s “Instructions for Authors” and revised all tables and figures accordingly. Specifically, we have: Added complete titles to all tables and figures. Included necessary footnotes and legends to explain abbreviations and symbols. Corrected in-text citations to ensure they follow the journal’s style. We also sought assistance from a colleague experienced in manuscript preparation to verify compliance. We believe the revised manuscript now fully meets the journal’s formatting standards.

3. Some tables in the appendices seem redundant. The table numbers in the manuscript and the appendices are overlapping, titles and notes are incomplete, and without any reference to the text of the manuscript. Some column headings are given in complete lower case. Requesting authors to be careful while preparing the manuscript even if those are supporting materials. Appendices are not meant for dumping of information and analyses, but to provide readers the context without confusing them.

Thank you for your detailed review of the appendices. We have carefully revised all supplementary materials according to your suggestions:

First, to avoid numbering overlap with the main text, the remaining appendices have been renumbered as S1 Table, S2 Table, and S1 Fig, with corresponding citations now added in the main manuscript.

Second, complete titles and detailed notes have been added to each table and figure, and all column headings have been corrected to proper case.

Third, regarding Appendix 2, we acknowledge that this appendix, which was created to address specific comments from Reviewer 6, did not provide meaningful information relevant to the main findings. Since these analyses are not cited in the main text, we have decided to remove Appendix 2 entirely to streamline the supplementary materials and avoid redundancy.

4. Method: Sample size calculation. Please remove the response rate from the paragraph. I believe all 248 pair of caregivers and PwD responded to the study, but the current analyses included data from 213 out of 248 pairs. This is not the response rate as we experience in survey where not all persons are approached may not respond.

Thank you for this important clarification. We agree that “response rate” is not the appropriate term in this context. We have revised the sentence to accurately reflect that the 213 dyads represent the final analytic sample. The revised text now reads:

Initially, 248 caregiver-PwD dyads were recruited. After excluding 35 dyads due to incomplete responses or other reasons, 213 valid dyads were included in the final analysis. (line 176-178)

5.Fig 2. Please mention the name of the score / scale, of which the cut-off of 15 was estimated, in the title of ROC curve.

Thank you for your suggestion. We have revised the title of Fig. 2 to clearly indicate the name of the scale used to estimate the cut-off value of 15. The revised title now reads: "Fig. 2. ROC curve of the Exemplary Care Scale for classifying low and high quality of care." (line 324)

6.Appendix-2 title of the ROC curve. Font not in English. No title of the figure. The ROC curve looks completely different what is provided in the manuscript (Fig 2). What is the full form of AUC.

Thank you for reviewing Appendix 2. This ROC curve was created in response to Reviewer 6's suggestion (Comment #48) to explore whether the ECS cutoff differs by sex. We conducted sex-specific ROC analyses using QoC as the classification variable. However, classification performance was poor (The area under the curve, AUC = 0.527), and no valid sex-specific cutoff could be established. Since these analyses did not provide meaningful information relevant to our main findings, and to avoid redundancy as per your guidance, we have decided to remove Appendix 2 entirely from the revised manuscript.

7. Table 8. Please include the name of the variable in the title of the table (like you did for Table 5) of which the mean and SD have been calculated.

Thank you for your guidance. The change has been marked in the revised manuscript. Table 8. Negative and positive groups classified by the optimal cut-off score of the Exemplary Care Scale. (line 325)

8. Specifically, contrary to both the model and prior research [7,16,41], caregiver depression, a core dimension of quality of life, did not show a significant association with QoC. Further, while explaining such non-significant association between depression and QoC author questioned the validity of the scale of depression in Chinese population, and inferred that the depression may be under-reported and that underreporting could be due to fear of social disapproval or strong internalized expectations among Chinese culture. At this point there are two situations. Either, the depression scale is not valid for the Chinese population, and we should not use the variable at all, or may be the non-significant result is the significance of the association. May be the caregivers are providing good quality of care to the PwD, as it is the cultural practice to care about them, in spite of themselves feeling depressed in providing care due to different socioeconomic constraints.

Thank you for this insightful observation. We acknowledge that there was a lack of clarity regarding the role of depression, which created confusion. We agree with the reviewer that simply dismissing the depression scale as invalid would be inconsistent with the data. We have softened this claim. Instead, we now argue that cultural factors may lead to either under-reporting or a decoupling of depressive feelings from caregiving behaviors. We have now substantially revised the Discussion section to address this:

This nonsignificant association can be attributed to Chinese cultural norms, particularly filial piety and family responsibility. This cultural value has been linked to reduced caregiver burden and enhanced QoC [42], as home-based dementia care in China is rooted in familism and filial norms [43]. Within this context, family caregivers may find fulfillment in their caregiving role [44], reinforced by community recognition, which enables them to sustain caregiving standards despite experiencing depressive symptoms. This mechanism explains our core finding: although depressive symptoms differed significantly across the three latent profile groups (S1 Table), they did not predict QoC. In other words, depression effectively captured caregiver heterogeneity, yet its impact on caregiving behavior was buffered by cultural responsibility. Thus, the observed nonsignificant association reflects the unique cultural context of family caregiving in China, rather than any inadequacy in scale validity. Future research should develop culturally sensitive models for assessing QoC. (line 355-368)

Attachment

Submitted filename: Response to Editor.doc

pone.0346557.s011.doc^{(43KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0346557.r007

Decision Letter 3

Arupendra Mozumdar

22 Mar 2026

Quality of informal care among informal caregivers of people with dementia : a latent profile and ROC analysis

PONE-D-25-38583R3

Dear Dr. Shi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Arupendra Mozumdar, Ph.D., M.Sc.

Academic Editor

PLOS One

Additional Editor Comments (optional):

I congratulate the authors for your handwork and resilient efforts they put behind the manuscript. Authors made satisfactory revisions to their manuscript by incorporating the suggestions put forward by the reviewers and editor. Thereby, I strongly recommend the paper to be accepted for the publication in the journal.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0346557.r008

Acceptance letter

Arupendra Mozumdar

PONE-D-25-38583R3

PLOS One

Dear Dr. Shi,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

Dr. Arupendra Mozumdar

Academic Editor

PLOS One

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Univariate analysis of different latent profile (N = 213).

(PDF)

pone.0346557.s001.pdf^{(110.3KB, pdf)}

S2 Table. Criterion values and coordinates of ROC Curve (validation subsample).

(PDF)

pone.0346557.s002.pdf^{(54.8KB, pdf)}

S1 Fig. ROC of the Exemplary Care Scale score for classifying low and high QoC.

(PDF)

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Quality of informal care among informal caregivers of people with dementia: A latent profile and ROC analysis

Chan Cai

Bing Cheng

Chongqing Shi

Wenli Shi

Chenyang Li

Cui Liu

Jin Sun

Roles

Abstract

Background

Methods

Results

Conclusions

1. Introduction

2. Materials and methods

2.1 Design and participants

2.2 Data collection

2.3 Sample size calculation

2.4 Ethics approval

2.5 Measures

2.5.1 Activities of daily living (ADL).

2.5.2 Quality of care (QoC).

2.5.3 Quality of pre-illness relationship.

2.5.4 Perceived overload.

2.5.5 Depression.

2.5.6 Social support.

2.6 Statistics analysis

3. Results

3.1 Sample characteristics

Table 1. General characteristics of PwD and caregivers (N = 213).

Table 2. Scores for caregivers’ quality of pre-illness relationship, perceived overload, and social support (N = 213).

3.2 Latent profile analysis

Table 3. Fit information of the latent profile models.

Table 4. Probability of different categories in the latent profile analysis.

Table 5. Means, standard deviations, and Cohen’s d for the three profiles of Exemplary Care Scale.

Fig 1. Mean item scores across the three-profile model of the Exemplary Care Scale. The figure displays the mean scores of each item across the three profiles.

3.3 Influencing factors analysis

Table 6. Results of multinomial logistic regressions predicting profile membership (R3STEP) (N = 213).

3.4 ROC analysis

Fig 2. ROC curve of the Exemplary Care Scale for classifying low and high quality of care.

Table 7. Criterion values and coordinates of ROC Curve (derivation subsample).

Table 8. Negative and positive groups classified by the optimal cut-off score of the Exemplary Care Scale.

Discussion

5. Limitations

6. Conclusion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

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Arupendra Mozumdar

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Author response to Decision Letter 1

Decision Letter 1

Arupendra Mozumdar

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Author response to Decision Letter 2

Decision Letter 2

Arupendra Mozumdar

Roles

Author response to Decision Letter 3

Decision Letter 3

Arupendra Mozumdar

Roles

Acceptance letter

Arupendra Mozumdar

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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