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.
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.
MATERIALS AND METHODS: This study is a quasi-experimental approach in which a non-equivalent control group was used in a post-test design. A comparison was carried out with two separate semester cohort students representing the control and intervention groups which had 24 and 30 students, respectively. This study included first-year nursing students that enrolled in a course called "Anatomy and Physiology" course of nursing education at a private university. The control group received all their teaching face-to-face, and the intervention group used information technology and prescribed activities in their online e-book. The self-directed learning readiness (SDLR) tool measures the learners' readiness in self-directed learning in both groups. This scale comprises three subscales which are "self-management," "desire for learning," and "self-control." An independent-samples t-test was conducted to compare self-directed learning readiness in the control and intervention groups. Data were analyzed using IBM SPSS Statistics 25 software to measure the independent t-test.
RESULTS: The self-directed readiness scores were significantly higher in the intervention group with P = 0.019. The intervention group showed a higher mean value on the subscales of self-management and self-control, which demonstrated a significant difference with P values of 0.018 and 0.028, respectively. The subscale desire for learning was insignificant with a P value of 0.166.
CONCLUSION: This study concluded that the overall results demonstrate that incorporating blended learning using e-books for anatomy and physiology courses in nursing education can contribute to students' readiness for self-directed learning. Specifically, the blended learning teaching and learning strategy had a positive impact on nursing students' capacity for self-management and self-control.
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.