Cancer pain self-management interventions in adults: scoping review.

BACKGROUND: The predominant trend in cancer treatment now leans towards outpatient care, placing the responsibility of pain management largely on the patients themselves. Moreover, a significant portion of treatment for advanced cancer occurs in the home environment, so patient self-management becomes increasingly crucial for the effective treatment of cancer pain. OBJECTIVES: To map self-management for pain in patients with cancer at all phases of the disease before examining the potential of pain self-care interventions for ill patients with cancer. METHODS: A search was conducted on six electronic databases to locate studies published in English, from 2013 to 2023. We followed Arskey and O'Malley's Scoping Reviews guidelines. RESULTS: This study thoroughly examined the provision of cancer pain self-management by healthcare professionals and identified four intervention types from 23 studies. Education emerged as the most prevalent form of self-management for cancer pain. CONCLUSION: Guiding patients in managing their pain effectively, starting from their hospitalisation and extending to their discharge.

Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models.

OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation. SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events. METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared. RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models. CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

Nursing practice environment, resilience, and intention to leave among critical care nurses.

BACKGROUND: Retaining experienced critical care nurses (CCNs) remains a challenge for health care organizations. Nursing practice environment and resilience are both seen as modifiable factors in ameliorating the impact on CCNs' intention to leave and have not yet been explored in Malaysia. AIMS AND OBJECTIVES: To assess the association between perceived nursing practice environment, resilience, and intention to leave among CCNs and to determine the effect of resilience on intention to leave after controlling for other independent variables. DESIGN: This was a cross-sectional survey. METHODS: The universal sampling method was used to recruit nurses from adult and paediatric (including neonatal) critical care units of a large public university hospital in Malaysia. Descriptive analysis and χ2 and hierarchical logistic regression tests were used to analyse the data. RESULTS: A total of 229 CCNs completed the self-administrated questionnaire. Of the nurses, 76.4% perceived their practice environment as being favourable, 54.1% were moderately resilient, and only 20% were intending to leave. The logistic regression model explained 13.1% of variance in intention to leave and suggested that being single, an unfavourable practice environment, and increasing resilience were significant predictors of nurses' intention to leave. CONCLUSION: This study found that an unfavourable practice environment is a strong predictor of intention to leave; however, further exploration is needed to explain the higher likelihood of expressing intention to leave among CCNs when their resilience level increases. RELEVANCE TO CLINICAL PRACTICE: Looking into staff allocation and equality of workload assignments may improve the perception of the work environment and help minimize intention to leave among nurses.