Gender differences in co-rumination and transition shock among nursing interns in China: a cross-sectional study DOI Creative Commons

Wenjie Ge,

Shou-Jun Zhu,

Xinyi Zhu

и другие.

BMC Nursing, Год журнала: 2025, Номер 24(1)

Опубликована: Апрель 15, 2025

It has been reported that co-rumination and transition shocks significantly influence effective communication in clinical practice. However, previous research not sufficiently explored the specific relationships between these two characteristics their gender differences among nursing interns. The objective of this study was to evaluate states shock current interns during placements, as well determine whether affect traits how exploiting such can improve nurses' co-rumination. A cross-sectional design adopted. We gathered data from a convenient sample 505 grade tertiary hospital Anhui, China. This included Data collected using Co-Rumination Questionnaire (CRQ-9) Transition Shock Scale for Undergraduate Nursing Students (UNSTS). were analyzed an independent samples t-test, Pearson correlation, hierarchical multiple linear regression. There no significant difference UNSTS scores male female interns, but had lower CRQ-9 (P < 0.05). found most critical factor influencing variation practice through regression analysis. Gender are reflected only level also shock. educators should be aware traits; is particularly important improving mental health problems based on students' aptitudes.

Язык: Английский

“Breaking barriers: The power of self-efficacy in combating occupational stigma and advancing gender equity in nursing education” DOI
Mohamed Hussein Ramadan Atta,

Asmaa Mohamed Ahmed Madkour,

Nagwa Ibrahim Hamad

и другие.

Nurse Education Today, Год журнала: 2025, Номер 149, С. 106632 - 106632

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

0
AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction DOI

Shajahan Wahed,

Mutaz Abdel Wahed

LatIA, Год журнала: 2025, Номер 3, С. 134 - 134

Опубликована: Март 1, 2025

Background: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental and overall well-being. Artificial intelligence (AI) offers innovative approaches predicting mitigating excessive internet use. Objective: This study aims develop evaluate AI-driven machine learning models for by analyzing behavioral patterns psychological indicators. Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” “Social Media Mental Health”, were analyzed using deep models, including Random Forest, XGBoost, Neural Networks, Natural Language Processing (NLP) techniques. Model performance was assessed based accuracy, precision, recall, F1-score, AUC-ROC. Results: Networks XGBoost achieved highest accuracy (91% 90%, respectively), surpassing traditional like Logistic Regression SVM. Clustering anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional thematic associated with addiction. Conclusion: effectively predict classify addiction, offering scalable personalized interventions promote Future research should focus addressing ethical concerns improving real-time deployment of these models.

Язык: Английский

Процитировано

0
AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction DOI

Shajahan Wahed,

Mutaz Abdel Wahed

LatIA, Год журнала: 2025, Номер 3, С. 73 - 73

Опубликована: Март 3, 2025

Background: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental and overall well-being. Artificial intelligence (AI) offers innovative approaches predicting mitigating excessive internet use. Objective: This study aims develop evaluate AI-driven machine learning models for by analyzing behavioral patterns psychological indicators. Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” “Social Media Mental Health”, were analyzed using deep models, including Random Forest, XGBoost, Neural Networks, Natural Language Processing (NLP) techniques. Model performance was assessed based accuracy, precision, recall, F1-score, AUC-ROC. Results: Networks XGBoost achieved highest accuracy (91% 90%, respectively), surpassing traditional like Logistic Regression SVM. Clustering anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional thematic associated with addiction. Conclusion: effectively predict classify addiction, offering scalable personalized interventions promote Future research should focus addressing ethical concerns improving real-time deployment of these models.

Язык: Английский

Процитировано

0
Gender differences in co-rumination and transition shock among nursing interns in China: a cross-sectional study DOI Creative Commons

Wenjie Ge,

Shou-Jun Zhu,

Xinyi Zhu

и другие.

BMC Nursing, Год журнала: 2025, Номер 24(1)

Опубликована: Апрель 15, 2025

It has been reported that co-rumination and transition shocks significantly influence effective communication in clinical practice. However, previous research not sufficiently explored the specific relationships between these two characteristics their gender differences among nursing interns. The objective of this study was to evaluate states shock current interns during placements, as well determine whether affect traits how exploiting such can improve nurses' co-rumination. A cross-sectional design adopted. We gathered data from a convenient sample 505 grade tertiary hospital Anhui, China. This included Data collected using Co-Rumination Questionnaire (CRQ-9) Transition Shock Scale for Undergraduate Nursing Students (UNSTS). were analyzed an independent samples t-test, Pearson correlation, hierarchical multiple linear regression. There no significant difference UNSTS scores male female interns, but had lower CRQ-9 (P < 0.05). found most critical factor influencing variation practice through regression analysis. Gender are reflected only level also shock. educators should be aware traits; is particularly important improving mental health problems based on students' aptitudes.

Язык: Английский

Процитировано

0