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To identify distinct social network types among young-old adults based on the characteristics of social network structure and to explore the relationship between different types, socio-demographic characteristics and subjective cognitive decline.
A cross-sectional study was conducted from July 2022 to October 2023.
A total of 652 young-old adults aged 60–74 years completed the sociodemographic questionnaire, the subjective cognitive decline questionnaire-9 and the self-designed egocentric social network questionnaire. The types of social networks were identified by latent profile analysis. Univariate analysis and binary logistic regression were used to analyse the influencing factors of subjective cognitive decline.
The incidence of subjective cognitive decline was 38%. Social networks of young-old adults tended to be large, predominantly family-centred and characterised by strong contact strength, high density and significant demographic heterogeneity among network members. Four social network types were identified: diverse-moderate, family-dense, family-strong and friend-loose. Young-old adults embedded in the family-dense and family-strong types were more likely to develop subjective cognitive decline than those in the diverse-moderate type. Additionally, age, education level, previous occupation, daily sleep duration and exercise were related to the incidence of subjective cognitive decline.
The findings highlight the relatively high incidence of subjective cognitive decline in young-old adults that is notably influenced by the type of social network they are embedded in. More attention needs to be paid to identifying and supporting young-old adults at high risk of subjective cognitive decline, especially to promote their social integration and friend network building, to improve their subjective cognitive function.
The findings emphasise the importance of considering the structure and composition of social networks when addressing subjective cognitive decline among young-old adults. A diversified social network incorporating both familial and friendship ties may provide enhanced cognitive protection. Therefore, interventions targeting subjective cognitive decline should promote the expansion of friendship-based relationships and foster the development of more heterogeneous and multi-source networks.
STROBE checklist.
Not applicable.
To explore how the mentor-student relationship affects nursing graduate students' satisfaction with mentors, as well as how mentoring mode and learning motivation work together.
A multi-centre cross-sectional study.
Thirty universities and colleges in eastern, central and western China.
A total of 826 nursing graduate students from thirty universities and colleges participated in this study in April 2024.
Data were collected using the general information questionnaire, mentor-student relationship entry, mentoring mode questionnaire, graduate students' satisfaction item and learning motivation scale. Data were analysed using SPSS 25.0 software. The PROCESS macro-plugin and the bootstrap method were utilised to examine the mediating and moderating effects of learning motivation and mentoring mode.
There was a positive correlation between nursing graduate students' satisfaction with mentors and the mentor-student relationship (r = 0.377, p < 0.001), learning motivation (r = 0.600, p < 0.001), and mentoring mode (r = 0.292, p 0.001). Learning motivation exerted a partial mediation effect between the mentor-student relationship and graduate students' satisfaction with mentors (mediation effect value = 0.182, 95% CI = 0.148–0.218). Mentoring mode moderated the path of learning motivation in the mentor-student relationship (interaction term coefficient = 0.031, 95% CI = 0.005–0.056).
Mentor-student relationship positively predicted nursing graduate students' satisfaction with mentors significantly. Learning motivation played a partial mediating effect between mentor-student relationship and graduate students' satisfaction with mentors and mentoring mode moderated between mentor-student relationship and learning motivation pathways. Therefore, cultivating positive teacher/helpful friend relationship, boosting students' learning motivation and improving mentoring mode techniques can all increase nursing graduate students' satisfaction with mentors.
No patient or public contribution.
To develop and validate a machine learning-based risk prediction model for delirium in older inpatients.
A prospective cohort study.
A prospective cohort study was conducted. Eighteen clinical features were prospectively collected from electronic medical records during hospitalisation to inform the model. Four machine learning algorithms were employed to develop and validate risk prediction models. The performance of all models in the training and test sets was evaluated using a combination of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, Brier score, and other metrics before selecting the best model for SHAP interpretation.
A total of 973 older inpatient data were utilised for model construction and validation. The AUC of four machine learning models in the training and test sets ranged from 0.869 to 0.992; the accuracy ranged from 0.931 to 0.962; and the sensitivity ranged from 0.564 to 0.997. Compared to other models, the Random Forest model exhibited the best overall performance with an AUC of 0.908 (95% CI, 0.848, 0.968), an accuracy of 0.935, a sensitivity of 0.992, and a Brier score of 0.053.
The machine learning model we developed and validated for predicting delirium in older inpatients demonstrated excellent predictive performance. This model has the potential to assist healthcare professionals in early diagnosis and support informed clinical decision-making.
By identifying patients at risk of delirium early, healthcare professionals can implement preventive measures and timely interventions, potentially reducing the incidence and severity of delirium. The model's ability to support informed clinical decision-making can lead to more personalised and effective care strategies, ultimately benefiting both patients and healthcare providers.
This study was reported in accordance with the TRIPOD statement.
No patient or public contribution.
The health communication ability of nurses significantly impacts patients' health positively. A strong knowledge base is essential for nurses to deliver high-quality health communication.
This study aims to explore the mechanisms linking nurse health knowledge acquisition and health communication ability.
A cross-sectional study.
This cross-sectional study utilised convenience sampling of 667 nurses from nine county-level hospitals. Questionnaires were used to assess health knowledge acquisition, health literacy, health education competence and health literacy communication ability in nurses. Structural equation modelling was employed to investigate the mechanisms linking nurse health knowledge acquisition and health literacy communication ability.
The correlation analysis revealed significant positive relationships among nurses' health knowledge acquisition, health literacy, health education competence and health communication ability. The chain-mediating model indicated that health knowledge acquisition significantly influences health communication ability, with a total effect, comprising a direct effect and an indirect effect. The indirect effects were mediated either independently by health education competence or through a combination of health literacy and health education competence.
A structural equation model was developed to provide a comprehensive framework for understanding the complex interplay among nurses' health knowledge acquisition, health literacy, health education competence and health communication ability. The model demonstrates that health knowledge acquisition has a significant overall effect and indirect effect on the improvement of health communication ability. Assisting nurses in translating health knowledge into health literacy may be a crucial step in enhancing their competence in health education.
These findings enhance our understanding of the predictive effects of health knowledge acquisition on health communication ability and offer practical implications for the promoting and intervening in the health communication ability of nurses.
STROBE statement.
No patient or public contribution.
The suicide rate of individuals with schizophrenia is higher than the general population. In clinical practice, it is essential to identify patients with schizophrenia who are at an elevated risk of suicide. However, previous studies may not fully account for potential factors that could influence the suicide risk among schizophrenia patients. Our study leverages machine learning to identify predictive variables from a broad range of indicators.
Cross-sectional.
A total of 131 patients with schizophrenia were recruited at the Mental Health Center of West China Hospital from August 2021 to July 2022. We collected complete blood analysis, thyroid function, inflammatory factors, childhood trauma experiences, psychological impact related to the Coronavirus Disease 2019 epidemic, sleep quality, psychological distress, income level and other demographic data. We utilised machine learning algorithms to predict the suicide risk of patients with the above features. The Shapley values were used to illustrate important predictive variables of suicide risk.
We gathered important variables for predicting suicide risk of patients with schizophrenia, such as the Nurses' Observation Scale for Inpatient Evaluation factor, neutrophil count, psychological impact during Coronavirus Disease 2019 epidemic, prolactin level and plasma thromboplastin component level.
The features identified in this study are anticipated to aid in the clinical identification of suicide risk in individuals with schizophrenia in the future. This study also promoted improvements in the suicide prediction model among patients with schizophrenia.
This study identified key predictive variables for suicide risk in schizophrenia patients using machine learning. Our findings will enhance clinical tools for assessing suicide risk in schizophrenia, potentially leading to more effective prevention strategies. This advancement holds promise for improving suicide prevention efforts and tailoring interventions to individuals' specific risk profiles.
STROBE Statement (for cross-sectional studies).
None.
This study investigates how observed workplace ostracism affects nurses' helping behaviour from a bystander's perspective, examining the mediating roles of moral courage and employee resilience to inform strategies for fostering workplace harmony in nursing settings.
A cross-sectional study design was adopted.
A survey of 346 nurses from two Grade III, Level A hospitals in Henan, China, utilised scales measuring workplace ostracism, moral courage, helping behaviour and employee resilience. SPSS Statistics 26.0, Mplus 8.3 and the SPSS macro program Process 4.1 plugin were used to test the associations among variables.
Observed workplace ostracism positively correlated with nurses' helping behaviour, with moral courage partially mediating this relationship. Employee resilience moderated both the link between observed workplace ostracism and moral courage, and the indirect effect of observed workplace ostracism on helping behaviour through moral courage.
Nurses with high levels of resilience demonstrate moral courage when observing workplace ostracism and engage in helping behaviours towards those ostracised.
This study examines how workplace ostracism undermines nursing team cohesion and individual well-being. It highlights that bolstering nurses' resilience and moral courage can alleviate these adverse effects, thereby improving patient care quality. Nursing managers are advised to adopt targeted strategies, such as resilience training, to mitigate workplace ostracism.
This study employs a questionnaire to explore nurses' views of workplace ostracism and helping behaviours, aiming to inform strategies for fostering nursing team harmony and improving care quality.
This study strictly follows the STROBE reporting guidelines to ensure the clarity and credibility of the research findings.
Data were collected from hospital nurses through electronic questionnaires.
Lower extremity lymphedema (LEL) is a debilitating complication for patients with gynecologic cancer. A series of strategies have been recommended to mitigate the risk of LEL and improve patient outcomes; however, investigation into LEL risk management behaviours in this population is limited, and the absence of reliable and valid tools is an important reason.
To develop and evaluate the psychometric properties of the lower extremity lymphedema risk management behaviours questionnaire (LELRMBQ) for Chinese patients with gynaecologic cancer.
This was a methodological study.
Initial items were generated using a literature review. The initial LELRMBQ was refined, and its content validity was evaluated by conducting two rounds of expert consultation and a pilot study. Psychometric testing of 389 participants recruited by convenience sampling was conducted from December 2022 to June 2023. Exploratory factor analysis (EFA; subsample 1, N = 158) and confirmatory factor analysis (CFA; subsample 2, N = 231) were performed separately to determine the multi-dimensional structure of the questionnaire. Known-group validity, internal consistency reliability, and test–retest reliability were also evaluated.
A total of 25 items with satisfactory content validity were included in psychometric testing. The EFA identified a four-factor structure, comprising 18 items, which explained 74.49% of the total variance. The CFA supported this structure with acceptable fit indices. Known-group validity was partially supported by significant differences in total LELRMBQ scores among groups with different education levels, residence, cancer type, and LEL awareness. Internal consistency and temporal stability were acceptable.
The 18-item LELRMBQ demonstrated sufficient reliability and validity as a tool for measuring LEL risk management behaviours in patients with gynaecologic cancer.
The LELRMBQ has potential applicability in assessing LEL risk management behaviours, identifying gaps in educational practices, tailoring effective interventions, and evaluating intervention effectiveness.
This manuscript followed the STROBE guidelines.
Patients with gynecologic cancer participated in this study and provided the data through the survey.
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