Skip to main content
NCBI home page
Lippincott Open Access logoLink to Lippincott Open Access
. 2025 Feb 11;74(3):186–192. doi: 10.1097/NNR.0000000000000813

Self-Advocacy Among Women With Uterine Malignancies

Xiaojie Chen 1,2,3,4,5,6, Xiaohan Xu 1,2,3,4,5,6, Yunhong Du 1,2,3,4,5,6, Wei Liu 1,2,3,4,5,6, Xiao Zhang 1,2,3,4,5,6, Li Wang 1,2,3,4,5,6
PMCID: PMC12036781  PMID: 39932437

Abstract

Background

Self-advocacy plays a crucial role in the mental health and treatment outcomes of oncology patients, particularly those with uterine malignancies. Despite its significance, research on the self-advocacy levels and influencing factors among Chinese patients with uterine malignancies remains limited.

Objectives

To assess the self-advocacy levels among Chinese patients with uterine malignancies and identify the demographic, psychological resilience, and decision self-efficacy factors that influence self-advocacy.

Methods

This cross-sectional study was conducted from March 1 to September 1, 2023, involving 220 inpatients with uterine malignancies from three tertiary hospitals in Shandong Province, China. Participants were recruited using convenience sampling and completed the General Information Questionnaire, Female Cancer Survivorship Self-advocacy Scale, Connor–Davidson Resilience Scale, and Decision Self-efficacy Scale.

Results

The average self-advocacy score among participants was 59.44 ± 10.14. Significant positive correlations were found between self-advocacy, psychological resilience, and decision self-efficacy. The random forest algorithm identified decision self-efficacy, psychological resilience, family average income, type of medical insurance, educational level, and residence as the six most important influencing factors, with the optimal model performance observed when lambda (λ) = 1.191. Multiple linear regression analysis further confirmed that decision self-efficacy, psychologic resilience, family average income, educational level, and residence were significant predictors of self-advocacy.

Discussion

The self-advocacy levels of Chinese patients with uterine malignancies were relatively low, with decision self-efficacy, psychological resilience, and socioeconomic factors significantly influencing their self-advocacy abilities. Future targeted interventions should focus on enhancing patients’ decision self-efficacy and psychological resilience, thereby guiding them to actively respond and participate in decision-making, ultimately improving self-advocacy among patients with uterine malignancies.

Key Words: cross-sectional study, decision self-efficacy, psychological resilience, random forest algorithm, self-advocacy, uterine malignancies

Uterine malignancies, including cervical and endometrial cancers, are common among women, particularly those aged 45 and older (Jiang et al., 2023). Globally, there were approximately 661,000 new cases of cervical cancer and 420,000 new cases of endometrial cancer in 2022, representing 8.4% of all female cancers (Bray et al., 2024). Despite advancements in screening and treatment, patients still face challenges such as physical and psychological trauma, long-term rehabilitation, and the risk of recurrence (Malmsten et al., 2019). Economic pressures, family changes, and evolving social roles also affect their quality of life, leading to a loss of autonomy in treatment and recovery (Dhakal et al., 2023). Enhancing patients’ self-advocacy abilities is, therefore, significant.

Self-advocacy refers to an individual’s ability to overcome obstacles and ensure that their preferences, needs, and values are met (Thomas et al., 2021); it is imperative for improving treatment outcomes, quality of life, and psychological well-being. According to Hagan and Donovan’s (2013) framework, self-advocacy consists of three core elements: informed decision-making, effective communication, and the power of connection. Informed decision-making helps patients choose appropriate treatments by weighing risks and benefits based on personal preferences (Faiman & Tariman, 2019). Effective communication involves collaboration between patients and health care professionals, ensuring patients express their needs and receive professional advice. The power of connection comes from social support networks, which provide emotional support and facilitate information sharing, helping patients adapt posttreatment (He et al., 2023). A lack of self-advocacy among patients with malignant uterine tumors can hinder communication and support, negatively affecting treatment outcomes and quality of life (Thomas et al., 2023).

Although research on self-advocacy among cancer patients exists in Western countries, it remains insufficient in China, particularly concerning patients with malignant uterine tumors. In China, influenced by Confucian culture, women’s family roles and responsibilities often restrict their autonomy in medical decisions (Meng et al., 2021). Furthermore, differences in China’s health care policies and social support systems lead to lower patient participation in decision-making compared to Western countries, where patients are more proactive (Kang et al., 2018). Understanding self-advocacy among Chinese patients can shed light on the influence of cultural contexts and inform targeted interventions.

Psychological resilience refers to an individual’s ability to demonstrate positive psychological adjustment in the face of adversity (Rutter 2012), and it acts as a mediating variable between stressors and mental health (Wu et al., 2020). A study on the mental health of caregivers revealed that psychological resilience is an essential resource for resisting psychological stress (Alonazi et al., 2023). Additionally, in multiple studies involving cancer survivors (Wang et al., 2024), rheumatoid arthritis patients (Zhou et al., 2024), and stroke patients (Diaz et al., 2021), psychological resilience has been shown to help regulate negative emotions, enhance coping abilities, and improve recovery prospects and quality of life. Likewise, individuals with high psychological resilience can effectively integrate and utilize social resources, further enhancing their coping abilities by seeking external assistance (Cao et al., 2024). These studies reveal the broad applicability of psychological resilience across different disease contexts, especially its protective role in mental health when facing long-term treatment and high-intensity stress. Anxiety and depression can limit an individual’s self-advocacy ability, while psychological resilience can reduce the occurrence and development of these emotions (He et al., 2022). While psychological resilience has been studied to some extent in other populations, there is still a lack of in-depth exploration of this trait in patients with malignant uterine tumors.

Decision self-efficacy refers to an individual’s decision-making ability during the decision-making process, reflecting the confidence and belief an individual has when facing complex decision situations (Hu et al., 2022). The concept has been extensively studied and validated across various populations and fields, including work identification (Xiao et al., 2016), health literacy (Smith et al., 2019), and medical decision conflicts (Lee & Bryant-Lukosius, 2023). In cancer treatment decisions, improved decision self-efficacy has been shown to help patients more actively participate in treatment choices, better assess risks and benefits, and thus improve treatment satisfaction and quality of life (Mei et al., 2022). The core of self-advocacy is the patient’s ability to make decisions for themselves. Patients with higher self-advocacy abilities are more likely to participate actively in treatment decisions, communicate effectively with health care professionals, and secure better treatment conditions for themselves (Thomas et al., 2021). Patients with malignant uterine tumors often need to face multiple critical medical decisions during their treatment, which not only involve the treatment plan for the disease but also affect the patient’s quality of life and psychological state. Therefore, enhancing patients’ decision self-efficacy is of great importance for strengthening their self-advocacy ability. In this study, we focused on the manifestation of decision self-efficacy in patients with malignant uterine tumors and explored its role in enhancing patient participation in treatment decision-making. Additionally, given that decision self-efficacy can be altered through interventions (Chen & Li, 2022), the study findings will also provide empirical support for the development of effective intervention strategies.

METHODS

Study Design and Participants

This cross-sectional survey followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines (von Elm et al., 2007), which ensure transparent reporting of confounding factors, bias, and generalizability, thereby improving study design, data collection, and analysis. This descriptive cross-sectional study employed convenience sampling and recruited inpatients diagnosed with uterine malignant tumors from three tertiary hospitals in Shandong Province. Inclusion criteria were inpatient diagnosed with uterine malignancy by pathological or cytological examination, age ≥18 years, adequate reading and writing ability, and voluntary participation in this study. Exclusion criteria were tumors of other systems or parts (second primary tumor), or severe mental or conscious disturbances. A total of 230 questionnaires were distributed, and 220 valid questionnaires were recovered, yielding a validity rate of 95.7%.

Procedures

Data were collected from March 1 to September 1, 2023. Data collection was completed by a researcher and two research assistants. Initially, the researcher trained the two assistants to ensure a full understanding of the questionnaire. With the consent of the ward manager, the two assistants personally distributed the surveys to the patients in the hospital. The requirements for completing the questionnaires were explained to the patients in a uniform language, and questions were answered in a standardized manner. For patients who were unable to fill out the questionnaires themselves due to low literacy, the assistants recorded and confirmed the answers on their behalf. All the questionnaires were recovered and verified on the spot.

Measures

Self-Compiled General Information Questionnaire

The general information questionnaire was developed based on literature review and expert consultation. It included sociodemographic factors (age, education, income, medical insurance, profession, and residence) and disease-related factors (chronic diseases, reproductive needs, abortions, cancer treatment, clinical stage, disease type, and duration).

Female Self-Advocacy in Cancer Survivorship Scale

The Female Self-Advocacy in Cancer Survivorship scale, developed by Hagan et al. (2016), consists of three dimensions in its Chinese version: self-determination, effective communication, and social support, with 18 items. The self-determination dimension (seven items) assesses patients’ decision-making abilities regarding their treatment; effective communication (six items) evaluates communication skills with health care providers; and social support (seven items) measures the ability to obtain support from society and family. Responses range from 0 (“strongly disagree”) to 5 (“strongly agree”), with higher scores indicating stronger self-advocacy. The overall Cronbach’s α for the scale is 0.819, with dimension-specific coefficients between 0.647 and 0.759 (Feng et al., 2021).

Connor–Davidson Resilience Scale

The Connor–Davidson Resilience Scale 10, developed by Campbell-Sills and Stein (2007), consists of 10 items assessing the adaptive capacity of individuals in response to stressful events. Using a five-point Likert scale (0 = “never” to 4 = “always”), higher scores indicate greater resilience. The Cronbach’s α coefficient is 0.91.

Decision Self-Efficacy Scale

The Decision Self-Efficacy Scale, developed by O’Connor (1995) and translated into Chinese by Wang et al. (2021), assesses an individual’s confidence in health care decisions. It includes 11 items on a five-point Likert scale (1 = “never” to 4 = “almost always”), with total scores ranging from 0 to 44. Higher scores indicate greater decision self-efficacy. Cronbach’s α coefficient in this study was 0.918.

Ethical Considerations

This study was reviewed and approved by our institution’s ethics committee. In accordance with the Helsinki Declaration, all patients provided written informed consent before participating in this study.

Statistical Analysis

All data were double-checked and analyzed using IBM SPSS Statistics (version 26.0) and R Studio (https://www.r-studio.com/). In descriptive statistics, categorical variables (e.g., age, education) were presented as frequencies and percentages, while continuous variables (e.g., self-advocacy scores, resilience scores) were expressed as mean ± SD (x ± s). Independent sample t-tests and analysis of variance were used to compare self-advocacy scores among different demographic groups, with post hoc tests conducted using least significant difference for homogeneity of variance and Games–Howell for heterogeneity. Pearson correlation was used to analyze relationships between variables. A random forest model was constructed in R Studio, where higher %Inc MSE (% Increase in Mean Squared Error) values indicated greater variable importance (Grömping, 2009). The %Inc MSE measures the increase in the model’s prediction error when the values of a specific variable are randomly permuted, with higher values indicating that the variable is more important for the model’s predictive accuracy. Least absolute shrinkage and selection operator (LASSO) analysis was applied for variable selection, with lambda.1se commonly chosen for the final model (Tibshirani, 1996). The selected variables were then analyzed using multiple stepwise regression, with statistical significance set at p < .05.

RESULTS

Sociodemographic Characteristics and Their Influence on Self-Advocacy

The total self-advocacy score for patients with uterine malignancies was 59.44 ± 10.14, with subscores for self-determination (20.11 ± 4.10), effective communication (17.8 ± 3.96), and social support (21.56 ± 4.51). Table 1 shows significant differences in self-advocacy scores based on age (F = 17.78, p < .001), education level (F = 51.93, p < .001), family income (F = 64.89, p < .001), medical insurance type (F = 45.83, p < .001), residence (F = 8.19, p < .001), chronic disease (t = 4.21, p = .001), and reproductive need (t = 3.56, p = .001).

TABLE 1.

Characteristics and Self-Advocacy (N = 220)

Variables n (%) Self-advocacy score (mean ± SD) t/F p Multitest
Age (years) ≤45(a) 64 (29.1) 64.67 ± 9.12 17.78 <.001 a > b, c; b > c
45–60(b) 131 (59.5) 58.27 ± 9.91
>60(c) 25 (11.4) 52.32 ± 7.41
Education level Junior school and below(a) 103 (46.8) 54.66 ± 8.10 51.93 <.001 c > a, b; a > b
High school(b) 87 (39.5) 60.75 ± 8.86
Bachelor and above(c) 30 (13.6) 72.07 ± 7.81
Family average income (RMB) <2000(a) 54 (24.5) 50.63 ± 9.52 64.89 <.001 c > a, b; a > b
2000–3999(b) 109 (49.6) 59.30 ± 7.39
≥4000(c) 57 (25.9) 68.05 ± 7.79
Medical insurance type NRCMI(a) 42 (19.1) 48.19 ± 4.99 45.83 <.001 a < b, c, d; b < c, d; e < b, c, d
URMI(b) 53 (24.1) 60.55 ± 7.95
UEMI(c) 61 (27.7) 64.56 ± 8.02
CMI(d) 48 (21.8) 65.13 ± 8.72
Self-pay(e) 16 (7.3) 48.75 ± 5.16
Professional Staff and workers of enterprise 70 (31.8) 60.59 ± 10.12 .57 .721 –
Farmers 31 (14.1) 59.94 ± 10.67
Self-employed 68 (30.9) 58.59 ± 9.06
Personnel of public institutions 7 (3.2) 60.14 ± 9.30
Retiree 11 (5.0) 55.82 ± 6.94
Unemployed 33 (15.0) 59.36 ± 12.78
Residence Rural area 97 (44.1) 53.92 ± 8.47 8.19 <.001 –
Urban area 123 (55.9) 63.80 ± 9.20
Chronic diseases Yes 120 (54.5) 61.98 ± 9.78 4.21 <.001 –
No 100 (45.5) 56.40 ± 9.76
Reproductive need Yes 5 (2.3) 75.00 ± 12.04 3.56 <.001 –
No 215 (97.7) 59.08 ± 9.83
Number of induced abortions ≤1 86 (39.1) 60.43 ± 10.26 0.81 0.446 –
2–3 122 (55.5) 58.95 ± 10.00
≥4 12 (5.5) 57.33 ± 10.91
Cancer treatment Radiotherapy 39 (17.7) 59.18 ± 9.80 .23 .707 –
Chemotherapy 116 (52.7) 59.13 ± 10.01
Surgery 65 (29.5) 60.15 ± 10.67
Tumor stage I 39 (17.7) 56.28 ± 8.41 2.34 .099 –
II 116 (52.7) 60.04 ± 10.67
III 65 (29.5) 60.26 ± 9.88
Types of disease Cervix 167 (75.9) 59.25 ± 9.50 .24 .624 –
Uterine corpus 53 (24.1) 60.04 ± 12.02
Course of disease ≤3 months 59 (26.8) 59.25 ± 10.98 .13 .878 –
3–6 months 99 (45.0) 59.20 ± 9.64
>6 months 62 (28.2) 60.00 ± 10.23
Open in a new tab

Note: SD indicates standard deviation; NRCMI, new rural cooperative medical insurance; URMI, urban resident medical insurance; UEMI, urban employee medical insurance; CMI, commercial medical insurance.

Correlation Between Self-Advocacy and Psychological Resilience and Decision Self-Efficacy

The mean scores for psychological resilience and decision self-efficacy were 28.0 ± 4.81 and 28.36 ± 5.84, respectively. Correlation analysis results showed that self-advocacy was significantly positively correlated with psychological resilience (r = .76, p < .001) and decision self-efficacy (r = .72, p < .001), psychological resilience was significantly positively correlated with decision self-efficacy (r = .65, p < .001).

Screening of Variables Influencing Self-Advocacy in Patients With Uterine Malignancy

Importance Ranking of Variables

Using self-advocacy as the dependent variable, significant variables were included in the random forest model. The ranking of importance was decision self-efficacy, psychological resilience, family income, medical insurance, education, residence, age, chronic disease, and reproductive needs. Details are shown in Figure 1 and Table 2.

FIGURE 1.

FIGURE 1

Open in a new tab

Importance ranking of influencing variables self-advocacy based on the random forest model. Note: this figure presents the results of variable selection using the LASSO method. As the lambda value increases, the regression coefficients of certain variables approach zero, indicating their diminishing importance in the model. The analysis highlights six key variables that significantly contribute to predicting self-advocacy, with decision self-efficacy and psychological resilience being the most influential.

TABLE 2.

Importance Ranking of Variables Influencing Self-Advocacy Based on the Random Forest Model (N = 220)

%IncMSE IncNodePurity Rank
Psychological resilience 28.576902 6101.032246 1
Decision self-efficacy 28.428330 6189.754559 2
Family average income 15.322232 2571.037658 3
Medical insurance type 13.098512 2225.853406 4
Education level 6.5099157 1603.946476 5
Residence 4.6781477 901.7469606 6
Age 4.4468052 1625.823906 7
Chronic diseases 0.4739310 223.2142678 8
Reproductive need 0.1921131 106.251142 9
Open in a new tab

Note: %IncMSE indicates % increase in mean squared error, measures the increase in the model’s prediction error when the values of a specific variable are randomly permuted. Higher values indicate greater importance of the variable in the model. IncNodePurity indicates increase in node purity, measures the total decrease in node impurity (e.g., Gini index) achieved by splitting on a specific variable across all trees in the forest. Higher values indicate greater importance of the variable in the model.

Screening of Variables

Based on the variable importance ranking, nine significant variables were analyzed using the glmnet function in R Studio for LASSO analysis. Details are shown in Figure 2. When λ = 1.191, the error was minimized, identifying six key variables. Due to collinearity, medical insurance type was excluded, leaving the final variables for multiple stepwise regression as decision self-efficacy, psychological resilience, family income, education, and residence.

FIGURE 2.

FIGURE 2

Open in a new tab

Characteristic variable screening based on LASSO analysis. Note: the figure illustrates the variable selection process of the LASSO regression model. The X-axis represents lambda (regularization strength), and the Y-axis represents the regression coefficients. As lambda increases, some coefficients shrink to zero. The two dashed lines indicate key lambda values: the left shows the minimum lambda (λ min), and the right shows λ 1se. In this study, six variables were selected using λ 1se.

Multiple Linear Regression Analysis for Self-Advocacy

A multiple stepwise regression analysis using self-advocacy as the dependent variable identified decision self-efficacy (β = 0.294, p < .001), psychological resilience (β = 0.361, p < .001), family income (β = 0.215, p < .001), education (β = 0.198, p < .001), and residence (β = 0.102, p = .007) as significant factors influencing self-advocacy in patients with uterine malignancies (Table 3).

TABLE 3.

Multiple Regression Analyses Predicting Self-Advocacy (N = 220)

Dependent variable Independent variable B SE β t p
Self-advocacy (Constant) 9.534 2.016 – 4.73 <.001
Psychological resilience .760 .093 .361 8.132 <.001
Family average income 3.054 .549 .215 5.564 <.001
Decision self-efficacy .511 .076 .294 6.765 <.001
Education level 2.844 .535 .198 5.319 <.001
Residence 2.075 .759 .102 2.734 .007
Open in a new tab

DISCUSSION

In this study, we found that the level of self-advocacy among Chinese patients with malignant uterine tumors was relatively low, with an average score of (59.44 ± 10.14), which is significantly lower than the results of similar studies in Western countries (Hagan et al., 2018), revealing a deficiency in self-advocacy capabilities among uterine cancer patients in Asian regions. This difference not only reflects the variations in cultural backgrounds and social norms but also reveals different approaches taken by health care systems regarding patient empowerment and involvement. Compared to Chinese breast cancer patients (He et al., 2023), the self-advocacy level of uterine cancer patients in this study was also relatively low. The analysis suggests that this is due to the specific social and psychological pressures faced by uterine cancer patients, such as the sensitivity of reproductive health. Also, traditional Chinese culture emphasizes values of female sacrifice over independence and autonomy (Meng et al., 2021), which to some extent, restricts patients’ proactivity and self-expression regarding health issues.

Among all dimensions of self-advocacy, we found that the effective communication dimension scored the lowest, which is lower than the results of several domestic studies (Fan et al., 2023; He et al., 2023), indicating significant barriers for patients with malignant uterine tumors in communicating with health care providers. This may be related to patients’ lack of understanding of the disease, leading them to overly rely on medical professionals’ opinions during the medical decision-making process. Moreover, the communication model between doctors and patients in China is primarily one-way, lacking patient feedback and active participation, which causes patients to easily compromise during the decision-making process and be unable to fully express their personal wishes and needs (Zhan et al., 2024). Although social support scores were relatively high, many patients tend to conceal their illness due to associations with reproductive system infections or sexual behavior, leading to feelings of shame (Gutusa & Roets, 2023). Furthermore, the treatment process can affect reproductive health and sexual life, making it difficult for patients to seek support, especially given the conservative views on fertility in Chinese society (Wang et al., 2022). In the self-decision dimension, the item “I can decide my cancer treatment on my own” scored the lowest, highlighting the passive role of Chinese women in medical decision-making. Decisions are often made jointly with family members, which neglect the patient’s own wishes and preferences while ensuring consistency and stability in medical services (Kang et al., 2018).

Factors Influencing Self-Advocacy in Patients With Uterine Malignancy

In our study, psychological resilience significantly positively influenced self-advocacy, consistent with Wang et al. (2024). Self-advocacy emphasizes actively striving for rights, while positive coping strategies are key protective factors of psychological resilience (Wu et al., 2020). The core of psychological resilience lies in helping patients effectively manage various stressors during the treatment process. Previous researchers have shown that psychological resilience can activate positive coping mechanisms, encouraging individuals to actively seek external support, thereby enhancing self-regulation and adaptability (Cao et al., 2024). Additionally, higher resilience helps patients build and maintain social support networks, enhancing confidence and positivity throughout the treatment (Yin et al., 2022).

Furthermore, resilience encourages continuous reflection and learning from setbacks, strengthening patients’ self-support capabilities over time (Alonazi et al., 2023). To enhance psychological resilience and self-advocacy, personalized interventions such as mindfulness meditation, breathing exercises, and peer support groups should be developed. These programs can help patients relieve stress, share experiences, and build collective self-help abilities (Diaz et al., 2021).

We found a significant positive correlation between decision self-efficacy and self-advocacy in patients with malignant uterine tumors, similar to Mei et al. (2022), who reported a positive correlation between decision self-efficacy and medical engagement. Patients with higher decision self-efficacy adopted proactive decision-making strategies, believing better decisions would improve their outcomes. Decision self-efficacy acts as a regulator of emotions and coping strategies, helping patients remain calm, analyze risks, and improve decision accuracy and confidence (Wang et al., 2024). It also encourages patients to seek information, evaluate options, and reduce decision conflicts, leading to choices that align with their health interests (Lee & Bryant-Lukosius, 2023). Moreover, decision self-efficacy enhances patient motivation, prompting them to actively express their preferences, which improves communication with health care providers and decision quality (Chen et al., 2022). Continuous communication and positive feedback from providers are essential to reinforce self-efficacy and promote proactive patient involvement in decision-making, thereby safeguarding their rights and preferences.

Sociodemographic variables, including educational level, family average income, and residence, have a significant effect on the self-advocacy ability of patients with malignant uterine tumors. Patients with higher education (bachelor’s degree or above) showed significantly better self-advocacy than those with junior high school education or below, consistent with previous studies (Fan et al., 2023). As an important indicator of an individual’s knowledge and information processing ability, education level enhances patients’ capacity for independent thinking and information processing, enabling them to participate more confidently in medical decisions and effectively express their needs and rights (Zhang et al., 2023). Given the long-term nature of cancer treatment, medical affordability and resource availability are crucial for patient adherence (Fan et al., 2023). In this study, patients with higher incomes and those in urban areas had significantly stronger self-advocacy compared to rural patients with weaker economic conditions, likely due to better access to medical resources (Fan et al., 2023; He et al., 2023). Rural patients faced limited treatment options, further inhibiting self-advocacy (He et al., 2022). Therefore, health care providers should communicate clearly to ensure patients understand their treatment plans, while considering their economic challenges and helping them access necessary resources. Additionally, enhancing patient–provider communication and listening to patients’ needs can build confidence and improve self-advocacy.

Limitations

Our study has several limitations. First, it was a cross-sectional study in that it cannot explain the long-term effects between self-advocacy and psychological resilience and other variables. Second, the measures used in this study rely on self-reported data from patients, which may introduce reporting biases. Third, the study was conducted in only three hospitals in Shandong Province, and the results may be limited in their generalizability due to regional and cultural constraints. Finally, we only analyzed psychological resilience and decision self-efficacy as influencing factors explaining self-advocacy variance. In the future, it is recommended to consider additional influencing factors, such as sense of shame, personality traits, and others.

CONCLUSION

The study results demonstrate that decision self-efficacy and psychological resilience play critical roles in the self-advocacy of patients with uterine malignancies. Combined with socioeconomic factors, these psychological attributes significantly influence patients’ participation in medical decision-making and self-advocacy levels. Future interventions should focus on enhancing patients’ decision-making confidence and psychological resilience to improve their active involvement in treatment decisions.

graphic file with name nres-74-186-g003.jpg

Open in a new tab

Footnotes

Acknowledgements: The authors would like to thank all the patients for their participation. This research was supported by the Traditional Chinese Medicine Science Project and Technology of Shandong Province (M-2022011).

The authors disclose no conflicts of interest.

Ethical Conduct of Research: This study was approved by the Review Board of Qingdao Hiser Hospital Affiliated of Qingdao University (Approval No. 2022HC08LS010).

Contributor Information

Xiaojie Chen, Email: 1429586956@qq.com.

Xiaohan Xu, Email: xxh15610707131@outlook.com.

Yunhong Du, Email: haiciduyunhong@163.com.

Wei Liu, Email: hiciliuwei@163.com.

Xiao Zhang, Email: haicizhangxiao@163.com.

REFERENCES

  1. Alonazi O. Alshowkan A., & Shdaifat E. (2023). The relationship between psychological resilience and professional quality of life among mental health nurses: A cross-sectional study. BMC Nursing, 22, 184. 10.1186/s12912-023-01346-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bray F. Laversanne M. Sung H. Ferlay J. Siegel R. L. Soerjomataram I., & Jemal A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74, 229–263. 10.3322/caac.21834 [DOI] [PubMed] [Google Scholar]
  3. Cao F. Li J. Xin W., & Cai N. (2024). Impact of social support on the resilience of youth: Mediating effects of coping styles. Frontiers in Public Health, 12, 1331813. 10.3389/fpubh.2024.1331813 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Campbell-Sills L., & Stein M. B. (2007). Psychometric analysis and refinement of the Connor–Davidson resilience scale (CD-RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress, 20, 1019–1028. 10.1002/jts.20271 [DOI] [PubMed] [Google Scholar]
  5. Chen X., & Li X. (2022). Research progress of patients’ decision self-efficacy in treatment decision-making. Chinese Journal of Practical Nursing, 38, 1588–1594. 10.3760/cma.j.cn211501-20211202-03321 [DOI] [Google Scholar]
  6. Diaz M. Cadet M., & Etienne C. (2021). Resilience protects against depression in people affected by stroke. Evidence-Based Nursing, 24, 135. 10.1136/ebnurs-2020-103338 [DOI] [PubMed] [Google Scholar]
  7. Dhakal K. Wang P. Mboineki J. F. Getu M. A. Chen C., & Shrestha D. L. (2023). The distinct experience of supportive care needs among cervical cancer patients: A qualitative study. Tumori, 109, 394–405. 10.1177/03008916221128064 [DOI] [PubMed] [Google Scholar]
  8. Faiman B., & Tariman J. D. (2019). Shared decision making: Improving patient outcomes by understanding the benefits of and barriers to effective communication. Clinical Journal of Oncology Nursing, 23, 540–542. 10.1188/19.CJON.540-542 [DOI] [PubMed] [Google Scholar]
  9. Fan Y. Li Y. Jia L., & Zhang L. (2023). Status quo and influencing factors of self-advocacy rights in female cancer patients. Chinese Nursing Research, 37, 897–903. 10.12102/j.issn.1009-6493.2023.05.025 [DOI] [Google Scholar]
  10. Feng L. Tan L., & Yu H. (2021). Chinese version of female self-advocacy in cancer survivorship and its reliability and validity test. Chinese Nursing Research, 35, 377–381. 10.12102/j.issn.1009-6493.2021.03.001 [DOI] [Google Scholar]
  11. Gutusa F., & Roets L. (2023). Early cervical cancer screening: The influence of culture and religion. African Journal of Primary Health Care & Family Medicine, 15, e1–e6. 10.4102/phcfm.v15i1.3776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Grömping U. (2009). Variable importance assessment in regression: Linear regression versus random forest. American Statistician, 63, 308–319. 10.1198/tast.2009.08199 [DOI] [Google Scholar]
  13. Hagan T. L., & Donovan H. S. (2013). Self-advocacy and cancer: A concept analysis. Journal of Advanced Nursing, 69, 2348–2359. 10.1111/jan.12084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hagan T. L. Cohen S. Stone C., & Donovan H. (2016). Theoretical to tangible: Creating a measure of self-advocacy for female cancer survivors. Journal of Nursing Measurement, 24, 428–441. 10.1891/1061-3749.24.3.428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hagan T. L. Gilbertson-White S. Cohen S. M. Temel J. S. Greer J. A., & Donovan H. S. (2018). Symptom burden and self-advocacy: Exploring the relationship among female cancer survivors. Clinical Journal of Oncology Nursing, 22, E23–E30. 10.1188/18.CJON.E23-E30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. He L. Hu L. Cui J. Yan D. Chu Y. Wang W., & Wang Q. (2023). Status and influencing factors of self-advocacy in breast cancer patients with chemotherapy. Chinese Journal of Nursing, 58, 788–793. 10.3761/j.issn.0254-1769.2023.07.003 [DOI] [Google Scholar]
  17. He L. Hu L., & Wang Q. (2022). Research progress on self-advocacy of female cancer survivors. Chinese Journal of Nursing, 18, 1673–1676. 10.16821/j.cnki.hsjx.2022.18.009 [DOI] [Google Scholar]
  18. Hu Y. Peng X., & Zhao J. (2022). Chinese translation and reliability and validity evaluation of the decision-making self-efficacy scale for family caregivers at the end of life with cancer. Military Nursing, 39(42–44), 48. 10.3969/j.issn.1008-9993.2022.04.011 [DOI] [Google Scholar]
  19. Jiang D. Zhu M. Zhao Q. Li J., & Sun H. (2023). Analysis of the incidence and death rate of female reproductive system tumors in China. Modern Oncology, 31, 4426–4431. 10.3969/j.issn.1672-4992.2023.23.025 [DOI] [Google Scholar]
  20. Kang X. Li Z., & Liu H. (2018). Analysis of patient participation in decision-making situation based on harmonious nursing theory. Chinese Journal of Nursing, 53, 498–501. 10.3761/j.issn.0254-1769.2018.04.024 [DOI] [Google Scholar]
  21. Lee M. K., & Bryant-Lukosius D. (2023). Information provision, decision self-efficacy, and decisional conflict in adopting health behaviors among patients treated for colorectal cancer: A cross-sectional study. Cancer Nursing, 46, 45–56. 10.1097/NCC.0000000000001040 [DOI] [PubMed] [Google Scholar]
  22. Malmsten C. Hellberg P. Bergmark K., & Dahm-Kähler P. (2019). Long-term fertility, oncological, and quality-of-life outcomes after trachelectomy in early stage cervical cancer. Archives of Gynecology and Obstetrics, 299, 1033–1041. 10.1007/s00404-018-4972-5 [DOI] [PubMed] [Google Scholar]
  23. Meng Y. Chen L., & Zou L. Q. (2021). Power, rights and culture: An implication from changes of medical decision-making mode. Journal of Nursing, 28, 74–78. 10.16460/j.issn1008-9969.2021.16.074 [DOI] [Google Scholar]
  24. Mei S. J. Duan P. P. Wang X. Q., & Yang L. H. (2022). Current state and influencing factors of decision fatigue in terminal ill cancer patients. Journal of Nursing Science, 22, 51–53. 10.3870/j.issn.1001-4152.2022.22.051 [DOI] [Google Scholar]
  25. O’Connor A. M. (1995). Validation of a decisional conflict scale. Medical Decision Making, 15, 25–30. 10.1177/0272989X9501500105 [DOI] [PubMed] [Google Scholar]
  26. Rutter M. (2012). Resilience as a dynamic concept. Development and Psychopathology, 24, 335–344. 10.1017/S0954579412000028 [DOI] [PubMed] [Google Scholar]
  27. Smith C. A. Chang E., & Gallego G. (2019). Complementary medicine use and health literacy in older Australians. Complementary Therapies in Medicine, 42, 53–58. 10.1016/j.ctim.2018.10.025 [DOI] [PubMed] [Google Scholar]
  28. Tibshirani R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 58, 267–288. 10.1111/j.2517-6161.1996.tb02080.x [DOI] [Google Scholar]
  29. Thomas T. H. Donovan H. S. Rosenzweig M. Q. Bender C. M., & Schenker Y. (2021). A conceptual framework of self-advocacy in women with cancer. Advances in Nursing Science, 44, E1–E13. 10.1097/ANS.0000000000000342 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Thomas T. H. Murray P. J. Rosenzweig M. Taylor S. Brufsky A. Bender C. Larkin M., & Schenker Y. (2023). “I was never one of those people who just jumped right in for me”: Patient perspectives on self-advocacy training for women with advanced cancer. Supportive Care in Cancer, 31, 96. 10.1007/s00520-022-07531-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. von Elm E. Altman D. G. Egger M. Pocock S. J. Gøtzsche P. C., & Vandenbroucke J. P., STROBE Initiative . (2007). Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. BMJ (Clinical research ed.), 335, 806–808. 10.1136/bmj.39335.541782.AD [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wang M. Wan Y. Geng L. Deng X., & Liu Q. (2022). Research progress on sexual dysfunction in infertile women. Journal of Nursing Science, 37, 104–107. 10.3870/j.issn.1001-4152.2022.19.104 [DOI] [Google Scholar]
  33. Wang S. Ye Z. Li Y. Liu F., & Li L. (2021). Reliability and validity testing of the Chinese version of decision self-efficacy scale in treatment decision-making treatment for patients with primary liver cancer. Journal of PLA Nursing, 38(37–40), 64. 10.3969/j.issn.1008-9993.2021.01.010 [DOI] [Google Scholar]
  34. Wang X. Li X. Zhang Z. Li Y. Zou Y. Yang L. Duan P., & Xu R. (2024). A latent profile analysis of psychological resilience in gastric cancer survivors: A cross-sectional study. European Journal of Oncology Nursing, 70, 102612. 10.1016/j.ejon.2024.102612 [DOI] [PubMed] [Google Scholar]
  35. Wu Y. Yu W. Wu X. Wan H. Wang Y., & Lu G. (2020). Psychological resilience and positive coping styles among Chinese undergraduate students: A cross-sectional study. BMC Psychol, 8, 79. 10.1186/s40359-020-00444-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Xiao Z. Peng Y. Feng W. Yang X. Chen Z., & Wang D. (2016). Current status and correlation analysis of career decision-making self-efficacy and professional identity among practicing nursing students. Journal of Nursing, 23, 58–62. 10.16460/j.issn1008-9969.2016.17.058 [DOI] [Google Scholar]
  37. Yin Y. Lyu M. Chen Y. Zhang J. Li H. Li H. Xia G., & Zhang J. (2022). Self-efficacy and positive coping mediate the relationship between social support and resilience in patients undergoing lung cancer treatment: A cross-sectional study. Frontiers in Psychology, 13, 953491. 10.3389/fpsyg.2022.953491 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zhan Y. Mao P. Gao F., & Shi Q. (2024). Content and duration of doctor–patient communication in outpatient oncology follow-up consultations in China. Cureus, 16, e55597. 10.7759/cureus.55597 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Zhang D. Xiong Y. Liu J. Li X. Shao J., & Xiong Y. (2023). Current situation of Chinese residents' core knowledge of cancer prevention and treatment and its influencing factors: A meta-analysis. Cancer Prevention and Treatment, 36, 476–485. 10.3969/j.issn.1674-0904.2023.06.004 [DOI] [Google Scholar]
  40. Zhou J. Gan Y. Fan X. Qi H. Cao Y., & Luo Z. (2024). Latent class analysis of psychological resilience and influencing factors in patients with rheumatoid arthritis: A cross-sectional survey. Clinical Rheumatology, 43, 1803–1813. 10.1007/s10067-024-06959-8 [DOI] [PubMed] [Google Scholar]

Articles from Nursing Research are provided here courtesy of Wolters Kluwer Health

RESOURCES