OBJECTIVE: This empirical study aimed to identify the impact of psychosocial job demands (emotional demands) and psychosocial job resources (health-specific leadership and social support of colleagues) on the psychological health (stress, burnout) of 284 Malaysian industrial workers (who participated both times).
METHODS: The Hierarchical regression analysis was employed to examine all study hypotheses and a time lagged study design was used with a lag of three months between T1 and T2 for data collection.
RESULTS: The survey data found a significant impact of emotional demands on stress and burnout, while we found insignificant findings of health-specific leadership and social support from colleagues on workers' psychological health.
FUTURE DIRECTIONS: Future studies should consider the different formations of psychosocial job resources and higher dimensions of health promotion leadership.
OBJECTIVE: This paper addresses this gap by investigating the relationship between employees and firms' environmental performance in the manufacturing sector operating in a developing country, Pakistan-where the environmental focus is sparse and organizational structures rarely follow cross-functional systems.
METHODS: Quantitative research was employed and SmartPLS technique was used to test the theoretical model with a valid response rate of 77 percent of senior and middle-level managers of manufacturing firms.
RESULTS: Results revealed the significance of green HRM as direct effect with all constructs. And internal environment management mediates the relationship (β = 0.158; t--value = 3.458; p
PATIENTS AND METHODS: This study was conducted in two phases: Phase I included the development of the multidomain intervention module iAGELESS and evaluation of content validity, while Phase II consisted of evaluating the acceptance of the module among 18 healthcare and social care providers, 13 older adults with cognitive frailty, and 13 caregivers. Content validity index (CVI) was used to quantify the content validity. Respondents completed a questionnaire which consisted of information on sociodemographic, followed by module acceptance evaluation with respect to content, terminologies, and graphics. The data was then analyzed descriptively.
RESULTS: A multidomain intervention module, iAGELESS was developed. The module was found to have appropriate content validity (overall CVI = 0.83). All the caregivers, 92% of older adults with cognitive frailty and 83% of healthcare and social care providers were satisfied with the overall content of the module. More than 50% of those who accepted the module had satisfactory consensus on the ease of the terminologies, length of sentences, pictures, information, color, and font size included in the module.
CONCLUSION: The iAGELESS module demonstrated good content validity and was well accepted, thus warranting its utilization in future studies to determine its effectiveness in reversing cognitive frailty among older adults.
OBJECTIVE: The aim of our present study is to determine the effectiveness of a comprehensive, multidomain intervention on CF; to evaluate its cost effectiveness and the factors influencing adherence toward this intensive intervention.
METHODS: A total of 1,000 community dwelling older adults, aged 60 years and above will be screened for CF. This randomized controlled trial involves recruitment of 330 older adults with CF from urban, semi-urban, and rural areas in Malaysia. Multidomain intervention comprised of physical, nutritional, cognitive, and psychosocial aspects will be provided to participants in the experimental group (n = 165). The control group (n = 165) will continue their usual care with their physician. Primary outcomes include CF status, physical function, psychosocial and nutritional status as well as cognitive performance. Vascular health and gut microbiome will be assessed using blood and stool samples. A 24-month intensive intervention will be prescribed to the participants and its sustainability will be assessed for the following 12 months. The effective intervention strategies will be integrated as a personalized telerehabilitation package for the reversal of CF for future use.
RESULTS: The multidomain intervention developed from this trial is expected to be cost effective compared to usual care as well as able is to reverse CF.
CONCLUSION: This project will be part of the World-Wide FINGERS (Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability) Network, of which common identifiable data will be shared and harmonized among the consortia.
METHODS: This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019. The study outcome was hospital all-cause mortality. Various machine learning models [logistic regression, random forest (RF), k-nearest neighbor, support vector machine, naïve Bayes, multilayer perception, boosting] were used to predict mortality, and their performance was measured using accuracy, precision, recall, F1 score, and area under the receiver operator characteristic curve (AUC).
RESULTS: Nonsurvivors had significantly greater EuroSCORE II values (1.84 (10.08-3.67) vs. 4.75 (2.54-9.53) %, P <0.001 for survivors and nonsurvivors, respectively). The EuroSCORE II score significantly predicted hospital mortality (OR: 1.13 (95% CI: 1.09-1.18), P <0.001), with an AUC of 0.736. RF achieved the best ML performance (accuracy=98, precision=100, recall=97, and F1 score=98). Explainable artificial intelligence using SHAP demonstrated the most important features as follows: preoperative lactate level, emergency surgery, chronic kidney disease (CKD), NSTEMI, nonsmoking status, and sex. QLattice identified lactate and CKD as the most important factors for predicting hospital mortality this patient group.
CONCLUSION: This study demonstrates the potential of ML, particularly the Random Forest, to accurately predict hospital mortality in patients undergoing CABG for LMCA disease and its superiority over traditional methods. The key risk factors identified, including preoperative lactate levels, emergency surgery, chronic kidney disease, NSTEMI, nonsmoking status, and sex, provide valuable insights for risk stratification and informed decision-making in this high-risk patient population. Additionally, incorporating newly identified risk factors into future risk-scoring systems can further improve mortality prediction accuracy.