Li Yang

This study aims to explore the relationship between financial toxicity (FT), fear of cancer recurrence (FCR), quality of life (QOL), and illness perception (IP) in breast cancer patients undergoing chemotherapy after a mastectomy, to provide new ideas and directions for improving their quality of life. A total of 305 breast cancer patients who received chemotherapy after a mastectomy were included in this study using the convenience sampling method. FT, FCR, IP, and QOL were cross-sectionally investigated using the General Information Questionnaire, Comprehensive Score for Financial Toxicity, Simplified Fear of Disease Progression Questionnaire, Functional Assessment of Cancer Therapy-General, and Brief Illness Perception Questionnaire. Data were analyzed using SPSS 26.0. Place of residence, employment status, household income per month, personal monthly income, age, literacy, and healthcare payment method, radiotherapy and surgical approach were found to affect the QOL of patients. QOL and FT scores were significantly correlated. FCR could mediate the effect of FT on QOL, and IP played a moderating role in the relationship between FT and QOL. The relationships between FT, FCR, IP, and QOL aligned with the findings of the stress response model. FT directly affects QOL and also further affects it via FCR, while IP modulates the effect of FT on the QOL, offering new insights for clinical staff tasked with choosing measures to improve the quality of life of patients.

Rehabilitation motivation is a critical determinant of recovery outcomes in stroke patients, yet its underlying profiles and associated factors remain insufficiently explored. This study aimed to delineate distinct latent profiles of rehabilitation motivation and examine their demographic and clinical correlates. Research Methods/Design: A total of 429 stroke patients were recruited from three tertiary hospitals in Qingdao, Shandong Province, China, between January and April 2022 using convenience sampling. Participants completed the General Information Questionnaire, Stroke Rehabilitation Motivation Scale, and Stroke Knowledge and Attitude Questionnaire. Latent profile analysis was conducted using the seven dimensions of Stroke Rehabilitation Motivation Scale to identify motivational subgroups, and their predictors were analyzed via multinomial logistic regression. Four unique motivation profiles were identified: Low group (7.7%), low introjected group (30.8%), balanced motivation group (28.6%), and high regulation group (32.9%). Significant differences (p < .05) were observed among groups in gender, age, employment status, education level, medical insurance, income, stroke frequency, primary caregiver, dizziness/headache symptoms, disease guidance, self-care ability, and Stroke Knowledge and Attitude Questionnaire scores. This study revealed four distinct rehabilitation motivation profiles and their predictors in stroke patients. These findings provide a foundation for clinicians to accurately identify patient subgroups and implement tailored interventions, ultimately optimizing rehabilitation motivation and improving recovery outcomes. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

BACKGROUND: Prompt hospital arrival is critical for acute ischemic stroke (AIS) treatment. This study aimed to examine the association between social network structure and prehospital delay among patients with AIS in China. METHODS: A cross-sectional survey using a validated instrument was conducted among patients with AIS admitted to 2 general hospitals in Qingdao, China, between October 2020 and May 2021. Key inclusion criteria were confirmed AIS diagnosis, age ≥18 years, and ability to complete the survey. Exclusion criteria included severe cognitive impairment or aphasia. We assessed 5 social network characteristics using egocentric network methodology: effective network size (number of nonredundant contacts), kin proportion (percentage of relatives), betweenness centrality (role as a bridge in the network), network constraint (limitations in forming new connections), and network efficiency (ability to access resources through contacts). Prehospital delay was defined as a binary outcome (arrival to hospital >3 hours after symptom onset). Binary logistic regression was used to analyze associations between network features and delay, adjusting for clinical and demographic variables. RESULTS: Among 422 participants (mean age 65.0±11.7 years, 71.0% male), 270 (64.0%) experienced prehospital delay (>3 hours). Adjusted logistic regression revealed that while a larger effective network size (odds ratio [OR], 0.39 [95% CI, 0.24–0.64]) and a higher kin proportion (OR, 0.21 [95% CI, 0.05–0.78]) were associated with reduced odds of delay, higher betweenness centrality (OR, 1.16 [95% CI, 1.03–1.31]), greater network constraint (OR, 1.02 [95% CI, 1.00–1.03]), and higher network efficiency (OR, 1.04 [95% CI, 1.02–1.06]) were associated with increased odds. CONCLUSIONS: Social network structure significantly influences prehospital delay in patients with AIS. Interventions focused on social network expansion and strengthening kin involvement may reduce delays and improve stroke outcomes.

Background: Psychosexual health refers to an individual’s capacity to acquire sexual knowledge through appropriate means, develop healthy values, accept bodily changes, align with socio-cultural norms and regulate sexual behaviors. The study aimed to explore the relative importance of general self-efficacy, sexual refusal self-efficacy, peer relationship, parental monitoring and parent–teen communication about sex in influencing psychosexual health among secondary vocational school students. Methods: A total of 2895 secondary vocational school students from of Shandong province in China were selected to complete the General Self-Efficacy Scale, Sexual Refusal Self-Efficacy Questionnaire, Peer Relationship Scale, Parental Monitoring Questionnaire, Parent–Teen Communication about Sex Questionnaire, School Connectedness Scale, School Sex Education Questionnaire and High School Student Psychosexual Health Scale. Dominance analysis was used for statistical analysis. Results: Dominance analysis revealed that importance rankings of the socio-ecological factors on psychosexual health in descending order were: peer relationship (the degree of contribution was 36.1%), parent–adolescent communication about sex (the degree of contribution was 34.2%), general self-efficacy (the degree of contribution was 14.5%), sexual refusal self-efficacy (the degree of contribution was 9.6%) and parental monitoring (the degree of contribution was 5.9%). Conclusions: Peer relationship, parent–adolescent communication about sex, general self-efficacy, sexual refusal self-efficacy and parental monitoring are the main socio-ecological factors on psychosexual health among secondary vocational school students. Accordingly, helping students build good peer relationships, facilitating appropriate parent–adolescent communication about sex and proper parental monitoring, and increasing students’ general self-efficacy and sexual refusal self-efficacy are crucial measures to improve psychosexual health for secondary vocational school students.

Introduction: The development of digital health technologies has had a positive impact on the management of comorbidity in the elderly. At the same time, it also sets certain requirements for the digital health literacy of older adults. To effectively implement interventions that improve digital health literacy among older adults with multimorbidity, it is essential to involve patients in decision-making processes and to develop interventions that align with their preferences. Methods and analysis: This study will use Discrete choice experiment to investigate the needs and preferences of older adults with multimorbidity for digital health literacy intervention. Attributes were identified through a literature review, in-depth interviews, and expert consultation. The attributes are intervention provider, content, type, frequency, material, learning mode, supplementary services, and cost. According to the identified attributes and levels, the choice set questionnaires were generated using Stata software. Furthermore, the study will assess the impact of demographic factors, social support, self-efficacy, levels of digital health literacy, and technology anxiety on the selection of preferences. A mixed logic model will analyze respondents’preferences for different intervention attributes and levels. A latent class model will identify heterogeneous group preferences. Results: We anticipate a minimum of 300 older adults with multimorbidity in community enrolling and completing data collection. Study results will be published in peer-reviewed scientific journals. Conclusions: The collection and analysis of patient preference information will greatly improve the accuracy and effectiveness of digital health literacy interventions in the development, adoption, and economic evaluation.

Objective: To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. Methods: A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. Results: The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. Conclusions: This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients’ health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.