Abstract
Background and purpose
Missed nursing care (MNC) are omissions that compromise patient care quality and are harder to detect than errors, posing greater risks to patient safety. However, current scales lack a cohesive framework, limiting the modelled association analysis between MNCs and self-reported causes. This study aims to classify MNCs, explore their relationships with self-reported causes, and examine the influence of sociodemographic factors among pediatric nurses.
Methods
A total of 459 pediatric nurses from 24 hospitals completed the Chinese-translated MISSCARE Survey-Ped. Exploratory factor analysis classified the MNCs, whose path analysis was subsequently performed based on self-reported causes. Sociodemographic factors were then analyzed to identify independent predictors across MNC quartile groups.
Results
The occurrence of pediatric MNCs was 67.7%, and the causes related to labor resources, material resources, and communication were reported at rates of 86.6%, 70.5%, and 72.0% respectively. Parallel analysis suggested a two-factor solution, which was subsequently extracted via EFA, classifying 28 MNCs into two distinct clusters in the Rotated Factor Space plot based on loadings > 0.4, characterized as Timely and Behavioral MNCs, respectively. Based on the two principal categories, a satisfactory path model was developed (CFI = 0.997, TLI = 0.979, RMSEA = 0.058, SRMR = 0.062). In this model, self-reported causes showed a positive overall effect on MNCs (Standardized-beta[95%CI] = 0.201[0.091, 0.311], P < 0.001). Negative effects were observed in path of labor resources → material resources (Standardized-beta[95%CI]=-0.578[-0.386, -0.770], P < 0.001), communication → labor resources (Standardized-beta[95%CI]=-0.120[-0.046, -0.194], P < 0.001), and communication → material resources (Standardized-beta[95%CI]=-0.856[-0.827, -0.885], P < 0.001). Additionally, Timely and Behavioral MNCs exhibited opposing mutual effects (Standardized-beta[95%CI]=-0.361[-0.216, -0.506] and 0.807[0.787, 0.827], both P < 0.001). Multivariate logistic regression revealed that factors such as communication, labor and material resources, position, education, fertility status, and hospital grade were independently linked to mild to severe MNC groups, with some showing negative correlations (OR = 0.433 ~ 0.525, all P < 0.05), and others positive correlations (OR = 1.068 ~ 1.799, all P < 0.05).
Conclusions
This study proposes a classification that divides MNCs into two categories, quantifies the modelled associations between MNCs and self-reported causes, and identifies independent factors that influence MNCs, offering evidence for effective interventions.
Implications for nursing management
Our innovative category of MNC behaviors supports path analysis and reveals the underlying modelled associations between MNCs and self-reported causes. As MNC severity increases, influencing factors follow a progression: individual factors → resource constraints → communication failure, offering insights for effective interventions.
Trial registration
National Universal Health Security Information Platform of National Health Commission (Registration No.: MR-51-25-037671, Date: Feb. 24, 2025).
Supplementary Information
The online version contains supplementary material available at 10.1186/s12912-025-04228-w.
Keywords: Pediatrics, Missed nursing care, Category, Cause, Structural modeling, Independent factor
Introduction
The effective implementation of nursing measures improves nursing quality and ensures patient safety. Missed nursing care was initially introduced by Kalisch in a qualitative study in 2006 [1], and subsequently defined in 2009 as any aspect of required nursing measures for patients that remain unfulfilled or delayed due to various reasons. This phenomenon is categorized as an omission behavior and has been recognized as a significant factor influencing the quality of nursing care [2]. The Agency for Healthcare Research and Quality highlights that omission behaviors are more challenging to detect compared to error behaviors and may present more significant safety concerns [3]– [4]. Worldwide, the phenomenon of MNCs is widespread. A recent systematic review [5] has confirmed that the occurrence of MNC is not influenced by the hospital type, scale, or location, nor is it associated with the characteristics of nursing staff, such as gender, age, experience, or educational level. Almost all nursing staff have experienced MNCs in their work. Numerous studies have demonstrated that 55% to 98% of nursing staff reported at least one omitted or incomplete nursing measure during their most recent shift [6]– [7], particularly basic care and nursing plans exceeded 70% [4, 8]. Currently, MNCs have emerged as a critical factor impacting patient safety and care quality [9]. It is not only associated with mortality, pressure ulcers, falls, infections, and readmission of patients [10] but also influences the job satisfaction and career identity of nurses [11]. In pediatric care, where children often cannot express their needs, the occurrence of MNC may result in more severe consequences. Studies from domestic and international single-center, multi-center, and large-scale investigations confirm the widespread issue of MNC in pediatrics [12–15].
The earliest scales applied to MNC investigations were the 2009 Nurse Version of the MISSCARE Survey [2] and the 2014 Patient Version of the MISSCARE Survey [16], both developed by Kalisch. The nurse version is divided into Behavior Subscale A (covering 22 common MNCs) and Cause Subscale B (including 3 dimensions of labor resources, material resources, and communication, with a total of 16 items). The patient version divides MNCs into three dimensions: communicative care, basic care, and timely care, with a total of 13 items. Patients self-report adverse events, including whether they experienced falls, skin injuries, medication errors, infections, and drug extravasation during their hospital stay. Based on the MISSCARE Survey, foreign scholars have successively developed two pediatric MNC scales, namely the MISSCARE Survey-NICU [14] and the MISSCARE Survey-Ped [17]. Considering children’s understanding and expression accuracy, along with parental or grandparental subjectivity, they have not developed a patient version. Compared to the MISSCARE Survey-NICU, designed for neonatal intensive care nurses, the MISSCARE Survey-Ped has a broader application scope as it is not restricted to neonatal populations. These two scales respectively account for the distinct physiological characteristics of neonates and elderly pediatric patients, as well as the specialized nature of pediatric care. The MISSCARE Survey-NICU addresses the neonatal nursing care needs of preterm and ill term infants, including oral feeding practices to support the transition from gavage to oral feeding, developmentally supportive care during hospitalization, and management of invasive lines, tubes, and drains [14]. The MISSCARE Survey-Ped incorporates family caregiver involvement in essential activities such as feeding, hygiene, play, and patient monitoring, aligning with the principles of family-centered care [17]. The MISSCARE Survey-Ped final version expands the Behavior Subscale A to 29 common pediatric care items and the Cause B scale to 17 potential influencing factors, reflecting the particularity of pediatric care. The practice application of this scale has demonstrated the severity of the pediatric MNCs as well as regional variations in their leading causes [18, 19]. However, all these scales investigate the MNCs only in a fragmented framework, rarely revealing the structural modeling between causes and behaviors.
To date, the majority of studies, whether in adult or pediatric populations, have focused on the MNC’s current status, as well as its consequences and influencing factors; interventional studies remain rare [5, 20–22]. Although a cross-national study spanning four countries confirmed that intervention measures were significantly effective in preventing MNC, it also emphasized that implementing these measures was complex and required system-level action [23]. The need to take complex measures may stem from the lack of clarity regarding the structural modeling between the Behavior Subscale A and the Cause Subscale B. In other words, there is a lack of in-depth correlational analysis to establish clear linkages between specific causes and particular MNCs. Although the primary MNCs cited in various reports are largely consistent, but their leading causes differ. Some attributes to labor resource causes, some attributes to material resource causes, while others attribute to communication causes [14–17, 24, 25]. Clarifying this structural modeling may potentially facilitate the simplification or optimization of interventions. Therefore, this study intends to classify the MNCs based on the pediatric nurse survey from the Behavior Subscale A, and explore their associations with the self-report causes from the Cause Subscale B. Additionally, this study categorizes the MNC degree into quartile groups to analyze the influence of self-reported reasons and sociodemographic data of pediatric nurses, providing evidence for further interventional studies.
Method
Study design and guidelines followed for its development
This study was registered as an investigator-initiated trial (IIT) and recorded in the National Universal Health Security Information Platform of National Health Commission (Registration No.: MR-51-25-037671, Date: Feb. 24, 2025).
Eligible population and inclusion criteria
In April 2025, pediatric nurses from 24 hospitals in Mianyang City were involved in this cross-sectional study. The inclusion criteria were as follows: (1) Hospital-registered nurses who were currently working in the Pediatric Department and held a nurse practice qualification certificate; (2) Nurses who had been engaged in pediatric nursing for more than one year; and (3) Voluntary participation and signing of informed consent. The exclusion criteria were as follows: (1) Nurses working in auxiliary, outpatient, and other non-clinical frontline positions; and (2) Student nurses, nurses in advanced training, and rotation nurses who left the pediatric department.
Following reference [26], the sample size was set at 10 times the number of items to maximize survey reliability, requiring at least 450 valid responses for the 45 items from the MISSCARE Survey-Ped. Accounting for an estimated 10% invalid response rate, at least 495 questionnaires should be distributed. Given that approximately 600 pediatric nurses were employed across 24 hospitals, each institution must ensure that at least 80% of eligible nurses participate to meet the required sample requirements.
Instrument
The original version of the MISSCARE Survey-Ped was developed by the Italian scholar Bagnasco in 2018. This study utilized the Chinese version translated by Chen et al. in 2024 [27]. The authors translated the original scale using the Brislin model (forward and backward translation, expert review, consultation with the original author) and conducted cultural adaptation through two rounds of expert consultation. According to expert opinion indicating that the job content of nurses in most Chinese pediatric wards was less involved in neurological development, they removed item A9 “Promoting neurological development based on children’s age and condition” from the Chinese version [27]. The Chinese version maintained the original structure, comprising two subscales: the Behavior Subscale A and the Cause Subscale B.
The Behavior Subscale A was used to assess the current situation of pediatric MNCs, consisting of 28 items scored on a Likert-5 scale from 1 (never missing) to 5 (always missing). Higher scores indicate a more severe absence of nursing activities. The reported Cronbach’s α was 0.944 and content validity was 0.971 [27], while in this study, Cronbach’s α was 0.963.
The Cause Subscale B surveyed the possible causes of pediatric MNC, including three dimensions (labor resources, material resources, and communication) with 17 items. Items are scored on a Likert-4 scale from “not a cause” (scoring 1) to “main cause” (scoring 4). Higher scores indicate a greater likelihood of being the main cause. The reported Cronbach’s α is 0.888 and content validity is 0.984 [27], while in this study, Cronbach’s α is 0.956.
In addition, the sociodemographic data of the participants were also surveyed, including gender, age, education, marital status, fertility status, professional title, working years, position, learning experience, demission intention, and hospital grade. The inclusion of marital status and fertility status in this study is justified by evidence indicating that marital status has been reported as a factor influencing MNCs [28], whereas fertility status may impact nurses’ psychological resilience, thereby affecting nursing quality [29].
Quality control of the survey process
To facilitate data collection, this survey adopted the WenjuXin (also called as Question Star), a Chinese online survey platform, to collect data. The cover page of the questionnaire explains the background, purpose, related concepts, filling methods, announcements, and admission principles of the survey, promises to keep the survey results confidential, and sets up an informed consent option. To prevent multiple responses, the questionnaire was set up to allow only one submission per mobile phone IP address, with single-choice answers for each item.
After obtaining consent from the pediatric head nurses in 24 hospitals via phone calls and/or WeChat, the head nurses distributed the questionnaire QR code in their work WeChat groups. After signing an informed consent form on the questionnaire cover page, nurses anonymously reported the occurrence of MNC during their latest shift. Before submission, all questions must be filled out without any omissions to ensure completeness. The criteria for determining valid responses were as follows: (1) the completion time should exceed 2.5 min to ensure a thorough reading of the questions; and (2) there should be no indication of regular response patterns.
Statistical analysis
Data analysis was conducted using the SPSS v26.0 software (IBM Corp., Armonk, NY, USA) and Scientific Platform Serving for Statistics Professional − SPSSPRO (Zhongyan Technology, Shanghai, China) (https://www.spsspro.com/mydata/index). The sociodemographic count data were presented in terms of frequency and percentage. The measurement data of scale scores were described by mean ± standard deviation. The non-missed and missed rates of pediatric MNCs were calculated as the percentages of questionnaires scored as 1 and those not scored as 1, respectively. Similarly, the denial and acceptance rates of pediatric MNC causes were calculated based on the same method. Principal component extraction for Behavior Subscale A was performed using the exploratory factor analysis (EFA) based on the polychoric correlations and oblimin rotation with Kaiser normalization, and further identified by the parallel analysis scree. Subsequently, pediatric MNCs were classified according to the rotated factor space plot. The path analysis was employed to explore the structural modeling between the Behavior Subscale A and Cause Subscale B. Finally, the quartile method was used to classify the pediatric MNC degrees, and their independent influence factors were investigated among the self-reported causes and sociodemographic characteristics of nurses by multinomial logistic regression analysis with backward elimination method for significant variable selection. The significance was evaluated using a two-sided test, and a p-value less than 0.05 was considered indicative of statistical significance.
Results
The sociodemographic data of pediatric nurses participating in the survey
A total of 459 pediatric nurses were involved in this survey. After excluding 11 with response times under 2.5 min and 8 with regular response patterns, 440 valid responses remained, representing a 95.9% response rate. Among these 440 pediatric nurses, the majority were aged 31 to 40 (accounting for 51.1%), had 5 to 15 years of working experience (accounting for 62.7%), held an undergraduate degree (accounting for 72.7%), and were senior nurses or charge nurses (accounting for 80.0%). For more detailed information, please refer to Table 1.
Table 1.
Sociodemographic characteristics of participants (n = 440)
| Characteristics | Category | n (%) |
|---|---|---|
| Sex | Male | 5 (1.1) |
| Female | 435 (98.9) | |
| Age | <=30years | 161 (36.6) |
| 31ཞ40 | 225 (51.1) | |
| > 40 years | 54 (12.3) | |
| Education | Junior college | 120 (27.3) |
| Undergraduate | 320 (72.7) | |
| Marriage status | Unmarried | 92 (20.9) |
| Married | 335 (76.1) | |
| Divorced | 13 (3.0) | |
| Fertility status | Nullipara | 123 (28.0) |
| Primiparity | 214 (48.6) | |
| Secundiparity or above | 103 (23.4) | |
| Professional title | Registered Nurse | 74 (16.8) |
| Senior Nurse | 162 (36.8) | |
| Charge Nurse | 190 (43.2) | |
| Deputy Chief Nurse | 14 (3.2) | |
| Working years | <5 years | 72 (16.4) |
| 5–10 years | 144 (32.7) | |
| 11–15 years | 132 (30.0) | |
| 16–20 years | 54 (12.3) | |
| >20 years | 38 (8.6) | |
| Position | Primary Nurse | 241 (54.8) |
| Team Leader | 67 (15.2) | |
| Clinical Support Nurse | 132 (30.0) | |
| Learning experience | Yes | 165 (37.5) |
| No | 275 (62.5) | |
| Demission intention | Yes | 40 (9.1) |
| No | 400 (90.9) | |
| Hospital grade | Tertiary | 348 (79.1) |
| Secondary or below | 92 (20.9) |
Percentages of missed MNCs and self-reported causes
This survey revealed that the overall mean score of the Behavior Subscale A was 38.39 ± 15.03, with the average score of each item ranging from 1.18 to 1.68 (Table 2). Furthermore, a total of 298 nurses (67.7%) reported at least one of 28 MNCs during their latest shift, with the missing rates (that is, the proportion of ≠ 1 score) ranging from 13.4% to 39.5% and the proportion of ≥ 3 scores ranging from 2.5% to 19.5%. The top 5 MNCs included psychological care, collaborative development of family care plans, assisting bedridden children with in-bed activities, supporting children in out-of-bed activities, and pain assessment. The bottom 5 MNCs included condition reassessment during shift change, infection control measures, vital sign monitoring, intake and excretion recording, and timely execution of temporary medical orders.
Table 2.
MNC behaviors of pediatric nurses (n = 440)
| Item | Score (mean ± SD) |
summed z-score | Non-missed rate [n (%)] |
Missed rate [n (%)] |
≥ 3 score [n (%)] |
|---|---|---|---|---|---|
| Behavior Subscale A | 38.39 ± 15.03 | 0.045 | 142(32.3) | 88(20.0) | NA |
| 1. Attendance at daily rounds at the bedside | 1.33 ± 0.77 | 0.055 | 352(80.0) | 154(35.0) | 38(8.6) |
| 2. Guidance and/or assistance for out-of-bed activities per nursing care plan, if conditions allow | 1.61 ± 0.97 | -0.352 | 286(65.0) | 134(30.5) | 76(17.3) |
| 3. Assess effectiveness of medications | 1.51 ± 0.90 | -0.009 | 306(69.5) | 160(36.4) | 65(14.8) |
| 4. Passive mobilization of child every 2 h or as ordered | 1.63 ± 0.99 | -0.010 | 280(63.6) | 135(30.7) | 79(18.0) |
| 5. Mouth care | 1.54 ± 0.97 | -0.201 | 305(69.3) | 115(26.1) | 66(15.0) |
| 6. Parents included in child’s care | 1.38 ± 0.75 | 0.192 | 325(73.9) | 94(21.4) | 35(8.0) |
| 7. Patient and family education | 1.32 ± 0.72 | 0.395 | 346(78.6) | 166(37.7) | 32(7.3) |
| 8. Discussion with child and his/her family about plans for discharge and care at home | 1.68 ± 1.04 | 0.038 | 274(62.3) | 143(32.5) | 86(19.5) |
| 9. Pain assessment per criteria when pharmacological or non-pharmacological analgesia | 1.56 ± 1.00 | -0.033 | 297(67.5) | 59(13.4) | 60(13.6) |
| 10. Promptly PRN orders based on the conditions | 1.18 ± 0.52 | 0.073 | 381(86.6) | 73(16.6) | 11(2.5) |
| 11. Promptly nursing records in compliance with writing criterion | 1.22 ± 0.57 | 0.308 | 367(83.4) | 80(18.2) | 16(3.6) |
| 12. Communication of all relevant information during shift change or handover | 1.24 ± 0.58 | 0.176 | 360(81.8) | 94(21.4) | 16(3.6) |
| 13. Satisfaction of eating need according to child’s clinical conditions, encourage a correct alimentation in according of personal taste | 1.28 ± 0.62 | -0.133 | 346(78.6) | 111(25.2) | 22(5.0) |
| 14. Medications administered within 30 min before or after scheduled time | 1.34 ± 0.67 | -0.096 | 329(74.8) | 141(32.0) | 32(7.3) |
| 15. Assist child with toileting needs within 5 min of request | 1.49 ± 0.84 | -0.204 | 299(68.0) | 119(27.0) | 55(12.5) |
| 16. Response to call light, to intervention request, or alarm | 1.39 ± 0.73 | 0.133 | 321(73.0) | 174(39.5) | 46(10.5) |
| 17. Psychological support for the child and/or family. | 1.64 ± 0.94 | 0.135 | 266(60.5) | 89(20.2) | 78(17.7) |
| 18. Labs/specimen obtained as ordered. | 1.28 ± 0.63 | -0.495 | 351(79.8) | 90(20.5) | 27(6.1) |
| 19. Bed care and skin care for children in need | 1.27 ± 0.61 | -0.036 | 350(79.5) | 85(19.3) | 25(5.7) |
| 20. Central line site and peripheral line site assessment per protocol. | 1.28 ± 0.70 | 0.079 | 355(80.7) | 78(17.7) | 23(5.2) |
| 21. Central line site and peripheral line site care per protocol | 1.27 ± 0.70 | -0.289 | 362(82.3) | 68(15.5) | 27(6.1) |
| 22. Adoption of the necessary precautions for infections control as per protocol | 1.21 ± 0.56 | -0.265 | 372(84.5) | 62(14.1) | 16(3.6) |
| 23. Monitoring intake/output of solid and liquid | 1.19 ± 0.54 | 0.211 | 378(85.9) | 66(15.0) | 17(3.9) |
| 24. Vital signs assessed according to the conditions | 1.20 ± 0.55 | 0.228 | 374(85.0) | 71(16.1) | 18(4.1) |
| 25. Focused reassessments of the child’s condition to assess improvements or deterioration during the shift | 1.22 ± 0.56 | -0.040 | 369(83.9) | 91(20.7) | 19(4.3) |
| 26. Hand washing correctly | 1.28 ± 0.62 | -0.477 | 349(79.3) | 119(27.0) | 27(6.1) |
| 27. Evaluate the quality of family care activities for the child. | 1.43 ± 0.84 | 0.007 | 321(73.0) | 117(26.6) | 50(11.4) |
| 28. Safety and hygiene checks of bedside facilities per regulations or at each shift | 1.42 ± 0.82 | 0.375 | 323(73.4) | 88(20.0) | 47(10.7) |
Note: SD, standard deviation; NA, not applicable. The scoring range is 28 to 140 for Behavior Subscale A, and 1 to 5 for each item. Non-missed (or missed) rate is defined as the percentage of responses equal to (or not equal to) 1 that are equal to (or not equal to) 1 for each item, with a value of 28 recorded for Behavior Subscale A. There are no missing values across all the 28 items
This survey revealed that the overall mean score of the Cause Subscale B was 37.50 ± 14.82, with an overall cause acceptance rate of 89.0% (Table 3). In comparison, the cause acceptance rate for labor resources was the highest, whereas the rate for material resources was the lowest. The top 3 causes included a shortage of nurses, a deficiency in support staff (e.g., nursing assistants), and an abrupt surge in patient volume and/or nursing tasks. The bottom 3 causes included an unprepared state of instruments and materials at the required time, an insufficient drug supply when needed, and a lack of familiarity with equipment, procedures, and regulations.
Table 3.
Self-reported causes of MNC from pediatric nurses (n = 440)
| Item | Score (mean ± SD) |
summed z-score | Denial rate [n (%)] | Acceptance rate [n (%)] | ≥ 3 score [n (%)] |
|---|---|---|---|---|---|
| Cause Subscale B | 37.50 ± 14.82 | -0.01 | 48(10.9) | 392(89.1) | NA |
| Labor resource | 15.09 ± 5.78 | 0.02 | 59(13.4) | 381(86.6) | NA |
| 1. Unexpected rise inpatient volume and/or acuity on the unit | 2.69 ± 1.25 | 0.19 | 127(28.9) | 313(71.1) | 259(58.9) |
| 2. Urgent patient situation | 2.18 ± 1.19 | 0.05 | 196(44.5) | 244(55.5) | 189(43.0) |
| 3. Inadequate number of nurses | 2.82 ± 1.19 | -0.16 | 100(22.7) | 340(77.3) | 282(64.1) |
| 4. Inadequate number of assistive | 2.68 ± 1.19 | 0.15 | 112(25.5) | 328(74.5) | 261(59.3) |
| 5. Unbalanced patient assignments | 2.29 ± 1.16 | 0.00 | 164(37.3) | 276(62.7) | 205(46.6) |
| 6. Frequent interruptions | 2.43 ± 1.14 | -0.14 | 130(29.5) | 310(70.5) | 216(49.1) |
| Material resource | 8.18 ± 3.98 | 0.02 | 130(29.5) | 310(70.5) | NA |
| 7. Supplies/equipment not available when needed | 2.11 ± 1.144 | 0.27 | 195(44.3) | 245(55.7) | 176(40.0) |
| 8. Supplies/equipment not properly functioning when needed | 1.99 ± 1.12 | 0.02 | 218(49.5) | 222(50.5) | 153(34.8) |
| 9. Medications were not available when needed | 2.04 ± 1.14 | -0.13 | 215(48.9) | 225(51.1) | 169(38.4) |
| Communication | 14.23 ± 6.73 | 0.00 | 123(28.0) | 317(72.0) | NA |
| 10. Lack of familiarity with equipment/procedure/policy | 2.04 ± 1.16 | -0.02 | 212(48.2) | 228(51.8) | 156(35.5) |
| 11. Tension or communication breakdowns within the nursing team | 1.99 ± 1.09 | -0.05 | 208(47.3) | 232(52.7) | 149(33.9) |
| 12. Lack of collaboration from team members | 2.09 ± 1.10 | 0.00 | 192(43.6) | 248(56.4) | 166(37.7) |
| 13. Nursing assistant did not communicate that care to the baby was not done | 2.10 ± 1.12 | -0.07 | 186(42.3) | 254(57.7) | 164(37.3) |
| 14. Tension or communication breakdowns with the medical staff | 1.99 ± 1.05 | -0.22 | 198(45.0) | 242(55.0) | 147(33.4) |
| 15. Tension or communication breakdowns with other services or departments | 1.96 ± 1.06 | 0.32 | 209(47.5) | 231(52.5) | 143(32.5) |
| 16. Other services or departments did not provide the care needed | 1.96 ± 1.04 | -0.08 | 202(45.9) | 238(54.1) | 141(32.0) |
| 17. Inadequate hand-off from previous shift or sending unit | 2.14 ± 1.15 | -0.11 | 189(43.0) | 251(57.0) | 175(39.8) |
Note: SD, standard deviation; NA, not applicable. The scoring range is 17 to 85 for Cause Subscale B, and 1 to 5 for each item. The acceptance cut-points is > 17 for Cause Subscale B, and > 1 for each item. Denial (or acceptance) rate is defined as the percentage of response that are equal to (or not equal to) 1 for each item, with values of 17, 6, 3, and 8 recorded for Cause Subscale B, labor resource, material resource, and Communication, respectively. There are no missing values across all the 17 items
Categorization of MNCs among pediatric nurses
To achieve subsequent structural modeling and correlation analysis for the purpose of exploring clear linkages between specific causes and particular MNCs, we conducted EFA on Behavior Subscale A. The EFA analysis indicated a Kaiser-Meyer-Olkin measure of 0.955 for the Behavior Subscale A survey, and Bartlett’s test of sphericity resulted in a Chi-square value of 12071.192 with P < 0.001, meeting the assumptions for further principal component analysis. The Scree plot extracted three factors with Eigenvalues greater than 1.0 (Fig. 1A), while the Parallel Analysis Scree plot based on the randomly generated mean and percentile eigenvalues identified two principal factors (Fig. 1B), accounting for cumulative variances of 63.50% in the initial eigenvalues and 61.11% in the extraction sums of squared loadings (1). The subsequent rotated factor space plot revealed a clear clustering of two distinct types of MNCs based on loading values exceeding 0.4, characterized by timeliness and behavioral attributes (Fig. 2; detailed EFA loadings, cross-loadings, and factor correlation are provided in Supplementary Table 2). Therefore, the 28 MNCs were classified into two categories: Timely MNC (including Item 4, 10–12, 14–16, 25) and Behavioral MNC (including Item 1–3, 5–9, 13, 17–24, 26–28).
Fig. 1.
Scree plot for principal component extraction. Note: (A) EFA Scree Plot; (B) Parallel Analysis Scree Plot. Based on the polychoric correlations and oblimin rotation with Kaiser normalization method, two principal components with Eigenvalues greater than 1.000 were extracted via EFA. Parallel analysis also suggested a two-factor solution
Fig. 2.
Rotated factor space plot for classifying MNCs. Note: Two clear, distinct clusters of MNCs were observed based on loading values exceeding 0.4, characterized by timeliness and behavioral attributes
Structural modeling between MNCs and causes among pediatric nurses
In all previous MISSSurvey-Ped reports, no analysis has been conducted on the intrinsic relationship between subscales A and B. To examine this relationship, path analysis, a method well-suited for scale-based investigations, was employed. Based on the bidirectional effect assumptions between subscales A and B, between Timely and Behavioral MNCs, along together among labor resource, material resource, and communication causes (derived from the inherent classification of Subscale B), the initial path analysis indicated four statistically insignificant paths (all P > 0.05), including “A score → B score”, “material resource → labor resource”, “material resource → communication”, and “labor resource → communication”. Consequently, these paths were excluded from further consideration.
The final model results revealed that (Fig. 3 and Supplementary Table 3): In the Cause Subscale B, labor resources negatively associated with material resources (standardized β=-0.578, P < 0.001), communication negatively associated with both labor and material resources (standardized β=-0.120 and − 0.856, both P < 0.001), and all three dimensions positively associated with the subscale B score (standardized β = 0.386, 0.274, and 0.445, all P < 0.001). In the Behavior Subscale A, Behavioral MNC positively associated with Timely MNC (standardized β = 0.807, P < 0.001), whereas Timely MNC negatively associated with Behavioral MNC (standardized β =-0.361, P < 0.001), and both dimensions positively associated with the subscale A score (standardized β = 0.702 and 0.310, both P < 0.001). Overall, the subscale B score positively associated with the subscale A score (standardized β = 0.201, P < 0.001). Except for the RMR value of 0.173 (out of < 0.05), other fit indices met their respective criteria (P = 0.276, χ2/df = 1.483, GFI = 0.995, RMSEA = 0.058, SRMR = 0.062, CFI = 0.997, TLI = 0.979, NFI = 0.995, and NNFI = 0.994), collectively indicating a good fit effect (Supplementary Table 4).
Fig. 3.
Path model of subscales A and B, along with their dimensions. Note: The path effect is presented as the estimate (95% confidence interval). Four insignificant paths (all P > 0.05) were excluded from this model, including “A score → B score”, “material resource → labor resource”, “material resource → communication”, and “labor resource → communication”
Influencing factors of quartile MNC degree groups
Furthermore, this survey also divided the participants into four different degree MNC groups based on the quartiles of the overall score of Behavior Subscale A: the Quar1 group (n = 161, with the overall score of 28), the Quar2 group (n = 79, with the overall score ranging from 29 to 31), the Quar3 group (n = 110, with the overall score ranging from 32 to 43), and the Quar4 group (n = 109, with the overall score ranging from 44 to 131). The multinomial logistic regression analysis with backward elimination method revealed that (Table 4), among the self-reported causes and sociodemographic characteristics of pediatric nurses, at least one factor was independently associated with varying degrees of MNC. These factors included communication, labor resources, material resources, position, education, fertility status, and hospital grade. Specifically, in the Quar2 group, only nullipara showed a negative association with MNC (OR = 0.433, P = 0.044); in the Quar3 group, labor and material resources, and junior college education were positively correlated with MNC (OR = 1.088, 1.154, and 1.799; P = 0.006, 0.045, and 0.042), while tertiary hospital was negatively associated with MNC (OR = 0.484, P = 0.029); and in the Quar4 group, communication was positively correlated with MNC (OR = 1.068, P = 0.001), whereas primary nurse and tertiary hospital were negatively correlated with MNC (OR = 0.525 and 0.434, P = 0.037 and 0.012).
Table 4.
Independent effects of Self-reported causes and sociodemographic characteristics on different degrees of MNC
| Influencing factor | Beta | SE | OR (95%CI) | Wald χ2 | P |
|---|---|---|---|---|---|
| Quar2 | |||||
| Constant | -0.230 | 0.611 | ND | 0.142 | 0.706 |
| Communication | -0.066 | 0.043 | 0.936(0.860, 1.018)) | 2.365 | 0.124 |
| Labor resources | -0.024 | 0.035 | 0.976(0.911, 1.046) | 0.464 | 0.496 |
| Material resources | 0.149 | 0.080 | 1.160(0.992, 1.357) | 3.475 | 0.062 |
| Position = Primary Nurse | 0.058 | 0.339 | 1.059(0.545, 2.061) | 0.029 | 0.865 |
| Position = Team Leader | 0.141 | 0.506 | 1.151(0.427, 3.105) | 0.078 | 0.780 |
| Position = Clinical Support Nurse | Ref | Ref | Ref | Ref | Ref |
| Education = Junior college | -0.268 | 0.326 | 0.765(0.403, 1.450) | 0.674 | 0.412 |
| Education = Undergraduate | Ref | Ref | Ref | Ref | Ref |
| Fertility status = Nullipara | -0.837 | 0.416 | 0.433(0.192, 0.979) | 4.043 | 0.044 |
| Fertility status = Primiparity | -0.250 | 0.354 | 0.779(0.389, 1.558) | 0.499 | 0.480 |
| Fertility status = Secundiparity or above | Ref | Ref | Ref | Ref | Ref |
| Hospital grade = Tertiary | 0.083 | 0.407 | 1.087(0.489, 2.415) | 0.042 | 0.838 |
| Hospital grade = Secondary or Primary | Ref | Ref | Ref | Ref | Ref |
| Quar3 | |||||
| Constant | 0.193 | 0.546 | ND | 0.124 | 0.724 |
| Communication | 0.038 | 0.039 | 1.039(0.962, 1.122) | 0.963 | 0.326 |
| Labor resources | 0.084 | 0.031 | 1.088(1.024, 1.156) | 7.458 | 0.006 |
| Material resources | 0.143 | 0.072 | 1.154(1.003, 1.328) | 4.009 | 0.045 |
| [Position = 1 Primary Nurse] | -0.098 | 0.311 | 0.907(0.493, 1.668) | 0.099 | 0.753 |
| [Position = 2 Team Leader] | 0.492 | 0.438 | 1.635(0.693, 3.860) | 1.260 | 0.262 |
| [Position = 3 Clinical Support Nurse] | Ref | Ref | Ref | Ref | Ref |
| [Education = 1 Junior college] | 0.587 | 0.273 | 1.799(1.055, 3.070) | 4.170 | 0.042 |
| [Education = 2 Undergraduate] | Ref | Ref | Ref | Ref | Ref |
| [Fertilitystatus = 1 Nullipara] | -0.515 | 0.372 | 0.598(0.288, 1.238) | 1.919 | 0.166 |
| [Fertilitystatus = 2 Primiparity] | -0.488 | 0.336 | 0.614(0.318, 1.186) | 2.107 | 0.147 |
| [Fertilitystatus = 3 Secundiparity or above] | Ref | Ref | Ref | Ref | Ref |
| [Hospitalgrade = 1 Tertiary] | -0.726 | 0.333 | 0.484(0.252, 0.930) | 4.743 | 0.029 |
| [Hospitalgrade = 2 Secondary or Primary] | Ref | Ref | Ref | Ref | Ref |
| Quar4 | |||||
| Constant | -0.413 | 0.563 | ND | 0.539 | 0.463 |
| Communication | 0.066 | 0.020 | 1.068(1.028, 1.110) | 11.259 | 0.001 |
| Labor resources | 0.048 | 0.033 | 1.049(0.984, 1.119) | 2.121 | 0.145 |
| Material resources | -0.034 | 0.072 | 0.967(0.840, 1.113) | 0.223 | 0.637 |
| [Position = 1 Primary Nurse] | -0.644 | 0.309 | 0.525(0.287, 0.962) | 4.344 | 0.037 |
| [Position = 2 Team Leader] | 0.186 | 0.425 | 1.205(0.523, 2.774) | 0.192 | 0.661 |
| [Position = 3 Clinical Support Nurse] | Ref | Ref | Ref | Ref | Ref |
| [Education = 1 Junior college] | -0.498 | 0.311 | 0.608(0.330, 1.117) | 2.572 | 0.109 |
| [Education = 2 Undergraduate] | Ref | Ref | Ref | Ref | Ref |
| [Fertilitystatus = 1 Nullipara] | -0.394 | 0.412 | 0.674(0.301, 1.512) | 0.916 | 0.338 |
| [Fertilitystatus = 2 Primiparity] | 0.256 | 0.353 | 1.292(0.647, 2.583) | 0.526 | 0.468 |
| [Fertilitystatus = 3 Secundiparity or above] | Ref | Ref | Ref | Ref | Ref |
| [Hospitalgrade = 1 Tertiary] | -0.835 | 0.331 | 0.434(0.227, 0.831) | 6.352 | 0.012 |
| [Hospitalgrade = 2 Secondary or Primary] | Ref | Ref | Ref | Ref | Ref |
|
Model fit Null model − 2 Log Likelihood = 1062.515, Full model − 2 Log Likelihood = 1101.700, Chi-squared = 87.538, df = 24, P < 0.001, Cox and Snell R2 = 0.180, Nagelkerke R2 = 0.193, AIC = 1155.700, BIC = 1266.043 | |||||
Note: With the Quar1 group as the reference. SE, standard error; OR, odds ratio; CI, confidence interval. “ND” refers to no data. “Ref” refers to the reference in the classification of this category
Discussion
The majority of studies on MNCs are predominantly focused on descriptive investigations of missing behaviors. Until now, unlike the subscale B of MISSCARE Survey-Ped, the subscale A has been defined as one-dimensional survey manner, and the 28 items have not yet been subjected to systematic evaluation for potential categorization. This gap has hindered further investigation into the intrinsic relationship between the A subscale and the B subscale. Therefore, we conceive a hypothesis to scientifically classify the 28 MNCs of subscale A. In response to this situation, this study identifies two categories of MNCs among pediatric nurses via the EFA analysis. Furthermore, this preliminary classification pattern enables the implementation of linear path analysis to quantitatively evaluate the structural modeling associations between the two categories of MNCs and the three categories of self-reported causes. These approaches establish a persuasive evidential model for identifying critical associational factors. Finally, the multivariate logistic regression analysis quantified how communication issues, labor and material resource shortages, and nurse characteristics (position, education, fertility status, and hospital tier) influence MNC severity. These findings provide a valuable reference for designing interventions to improve MNC.
Missed rate of MNCs among pediatric nurses is similar to previous studies
This survey uses the score proportion method to calculate the MNC missed rate, which clearly distinguishes MNC levels (non-missed, rarely missed, occasionally missed, often missed, always missed) and provides a more accurate assessment than the average score method. After excluding non-missed responses, the missed rate is 67.7%, slightly lower than most domestic and international pediatric studies [12, 21, 30], primarily due to differences in calculation methods. Using the average score method, the rate rises to 72.9%, yielding a similar conclusion. Overall, both this and previous studies confirm that most pediatric nurses failed to complete at least one nursing task during their last shift.
This survey shows that top MNCs mainly focus on basic care, which is consistent with the findings of several studies [7, 15, 31], including psychological care, family care planning, in-bed and out-of-bed activities for children, and pain assessment. However, when nurses face heavy workloads, they may overlook these aspects by prioritizing more urgent or visible tasks [32]. The lower performance in bottom MNCs supports this explanation. Vital sign monitoring, intake and output recording, and timely execution of temporary orders are consistently documented because omissions are easily detected. Condition reassessment during shift change is critical, as any issues missed after handover become the responsibility of the incoming nurse. Additionally, the low rate of missed infection control practices contrasts with other studies, likely due to strong emphasis on training and strict quality controls in our region. These results suggest pediatric units should strengthen MNC training, promote family-centered care, address psychosocial needs, and reduce missed care to improve quality.
Shortage of labor resources − the main self-reported cause in this MNC survey
This survey finds material resources rated lowest among self-reported causes, contrasting with prior studies that identify communication as the weakest lowest one [15, 21, 22]. This discrepancy may be attributed to the sufficient reserves of instruments, equipment, materials, and medicines in this region. Labor resources scored highest here, though findings vary across studies [26, 30], likely due to regional differences in talent development, labor demand, and resource allocation. Among 17 causes, the top three highlight the significance of labor resources in this region, including nurse shortages, insufficient assistive staff, and unexpected increases in patient volume or acuity. Despite steady growth in nursing staff overall in recent years, pediatric nursing faces a severe shortage due to rising social demand and the implementation of two- or three-child policy, increasing work pressure [33, 34]. Furthermore, the unique characteristics of pediatric care, such as more difficult illness assessment, substantial observational responsibilities, diverse nursing tasks, and high operational complexity, exacerbate the impact of labor resource constraints on MNCs [22]. Therefore, to reduce MNCs and improve care quality, efforts should focus on strengthening nurse training, enhancing salaries and benefits to attract specialists, and promoting humanistic and professional education to build skills.
Path analysis of MNCs and causes to explore their structural modeling
An in-depth structural modeling analysis between the MNCs and self-reported causes can provide an effective means for distilling an intervention improvement framework. This survey identifies two categories of MNCs. According to this classification, path analysis reveals three important relationships between the behaviors and causes of MNCs: (1) the theoretical model of competing demands between the two types of MNCs, (2) the internal vicious cycle mechanism of MNC causes, and (3) the self-reinforcing effect on MNC.
The negative path coefficient between Timely and Behavioral MNCs is consistent with a theoretical model of competing demands. The Behavioral MNC can exacerbate the Timely MNC, potentially, since once Behavioral MNCs arise, nurses require additional time to address and rectify previous nursing deficiencies. The Timely MNC instead suppresses the Behavioral MNC, as nurses are usually compelled to prioritize time-critical tasks under workload pressures. This may lead to neglecting non-urgent measures like health education and psychological care, resulting in Behavioral MNC. This competing demands highlights the competitive nature of pediatric care tasks; that is, time-critical tasks (such as medication administration and alarm response) and process-oriented tasks (such as health education and psychological care) are in competition for labor resources. However, the effect of Behavioral MNC scores is more pronounced than that of Timely MNC scores in elevating the subscale A scores, indicating that the omission of non-urgent care might represent a significant shortcoming of current pediatric care quality in this region.
The internal vicious cycle mechanism of MNC causes is manifested as follows: the shortage of labor resources intensifies the relative scarcity of material resources (due to a lack of personnel to utilize them), while communication barriers not only directly hinder the efficient allocation of labor resources but also further reduce the accessibility and effective utilization of material resources. This finding is consistent with the conservation of resources theory [35]: (1) nursing staff experiencing long-term manpower shortages face energy depletion, reducing their ability to manage materials and coordinate equipment; (2) and poor communication further decreases team collaboration efficiency; and (3) the two types of causes gradually create a vicious cycle of “labor shortage → communication failure → loss control over materials”. Obviously, among the three types of causes that push up the subscale B scores, labor resource and communication issues are the core drivers of MNCs. This conclusion is supported by prior studies [7, 15, 21, 22, 31].
The key evidence of the self-reinforcing effect on MNC is that the subscale B scores significantly positively impact the subscale A scores, highlighting the severity of labor resources, material resources, and communication issues. Failure to timely implement the care plan may lead to deterioration of the patient’s condition, increase the need for emergency rescue, and further consume labor and material resources; omissions in measures may cause dissatisfaction among family members and intensify the communication conflicts between nurses and patients [1, 5, 36]. According to the dynamic deterioration process of the Donabedian quality theory [37], any MNC may further form a closed loop of “omission → resource depletion → more serious omission”, and local MNCs are prone to develop into a systemic quality decline.
Based on these findings, an intervention framework can be developed at three levels to address MNC problems. First, establish MNC warning indicators and allocate resources in real-time to prevent the deterioration from labor resources to material resources. Second, improve communication efficiency by standardizing shift handovers and introducing a multidisciplinary platform to reduce negative impacts on labor and material resources. Finally, focus on the intervention of the most influential Behavioral MNCs, and implement checklists and electronic reminders for non-urgent care, like health education and psychological support.
MNC degree: more conducive to analyzing the gradual progress of influencing factors
After quartile stratification, the influencing factors of MNCs demonstrate a gradual process characterized by individual factors→resource constraints→communication failure. Specifically, in the mild MNC group (Quar2), only the nulliparous status is found to be protective against MNCs. Such nurses can focus more on their work because of a lighter family burden, but vigilance is needed to avoid nursing omissions due to their lack of experience. In the moderate MNC group (Quar3), labor shortages and material scarcity begin to pose significant risks for MNCs, and junior college-educated nurses may be more susceptible to encountering MNC-related challenges due to insufficient training or inadequate knowledge structures, whereas nurses in tertiary hospitals may have an advantage in resisting MNC risks due to a more complete resource allocation system. In the severe MNC group (Quar4), communication failure becomes the top MNC risk, while nurses in tertiary hospitals demonstrate a stronger protective advantage, and primary nurses begin to demonstrate resilience against MNC risks. Among the three causes (labor resources, material resources, and communication), only communication failure shows a consistently increasing OR value with the degree of MNC, eventually demonstrating its sole risk advantage in severe MNCs. This highlights its significance in MNC progression. Multiple studies confirm that communication failures from colleagues or patients impact MNCs more significantly than labor and material resource issues [38–40]. Recent reports indicate that communication failure acts as an amplifier in the vicious cycle of MNCs, worsening the already severe MNC phenomenon [41].
Noteworthily, the influence of hospital grade, position, and education on MNC remains controversial [42–44], with limited reports on fertility status. In this survey, tertiary hospitals show a protective effect in the moderate to severe MNC groups, highlighting the importance of labor reserve, materials dispatch, and communication mechanisms. The protective advantage of primary nurses in the severe MNC group highlights the importance of all frontline nurses being familiar with nursing standards and quality requirements, regardless of clear positions. Junior college education may increase risk in moderate MNC cases due to an ability-demand mismatch, emphasizing the need for enhanced competency and skill development. Nulliparous nurses, who tend to be younger, demonstrate a protective advantage exclusively within the mild MNC group, and no correlation within moderate to severe groups suggests that limited experience outweighs high energy. These findings imply that strengthening their competencies and skills is essential, given their double-edged nature of high energy coupled with limited experience.
Limitations: (1) This regional cross-sectional study inevitably encompasses spatial disparities, as well as racial and regional variations, requiring further multi-regional longitudinal studies to validate its conclusion; (2) The deleted item A9 in the Chinese version has resulted in the absence of a neurodevelopmental care assessment, thereby inevitably compromising comparability with international studies; (3) Self-reports are inevitably influenced by subjectivity, further associating with the clinical outcomes could facilitate a more in-depth exploration of their clinical relevance; (4) The discrepancy between RMR and SRMR may originate from the covariance matrix derived from Likert-scale data, variability of observed values, and whether standardization, indicating that model fit indices should be carefully evaluated and appropriately selected, with consideration of differences in measurement units and variances to ensure a more accurate assessment of model fit; (5) The preliminary classification of the two MNC categories requires confirmation in an independent sample through confirmatory factor analysis, and its generalizability across different countries and regions remains uncertain, necessitating future large-scale, multi-center studies to validate their robustness and cross-cultural applicability; (6) Single instrument alone is insufficient for conducting a comprehensive assessment of MNCs, highlighting the need to further enhance its integration and collaborative use with other clinical evaluations, such as nurse psychological scales, healthcare quality, and patient safety; (7) Due to privacy protection purposes, cluster effect analysis could not be conducted on the data from 24 hospitals, highlighting the necessity of formulating a clear plan to collect cluster data in future research; (8) Backward elimination can facilitate the construction of a parsimonious model focused on significant variables but is vulnerable to multicollinearity, heteroscedasticity, and autocorrelation, etc. Future confirmatory studies should select variables based on a pre-specified theoretical framework to enhance model robustness and interpretability.
Conclusion
Based on surveying the current situation of pediatric MNCs, this study proposes a classification that divides the 28 items of the Behavior Checklist A into Timely and Behavioral MNCs, and attempts to reveal the mutual reverse effects between them, as well as the path effects between MNC behaviors and their causes. However, this two-factor structure requires confirmation in an independent sample through CFA before it can be considered a robust framework. In addition, this study also explores the influence factors on mild, moderate, and severe MNCs from the self-reported causes and sociodemographic characteristics of nurses, revealing a gradual process characterized by individual factors→resource constraints→communication failure. These findings can provide supporting evidence for targeted interventions and offer new solutions for pediatric MNC challenges.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
S.X., Y.Q., and Y.Y. proposed the conceptualization and obtained funding support. X.C. and M.D. wrote the main text. F.Y., F.W., and Y.Z. performed the investigation and conducted the data curation. Y.Y. graphed the figures. All the authors reviewed the manuscript. S.X. and M.D. contributed equally to this work.
Funding
This study was supported by the Mianyang Central Hospital School of Medicine, University of Electronic Science and Technology of China (MR-51-25-037671). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability
The raw data used in this study are available from the authors upon reasonable request.
Declarations
Ethics and consent to participate
This study was approved by the Ethics Committee of Mianyang Central Hospital in accordance with the principles of the Declaration of Helsinki (Approval No.: S20250333-01). All the subjects provided signed informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shuai Xie and Mengqin Dai contributed equally to this work.
Contributor Information
Yun Qiu, Email: 1606585803@qq.com.
Xiaohong Chen, Email: xiaohongch@aliyun.com.
Yuwei Yang, Email: yyw318@sc-mch.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The raw data used in this study are available from the authors upon reasonable request.