Introduction: Psychosocial stressors appear to alter the state of mind and adoption of overeating behaviour, resulting in high body mass index. This study was conducted to determine the magnitude of psychosocial stressors on male employees' well-being. Method: This study used secondary data retrieved from a cross-sectional study involving 492 male employees' completed data. Eligible participants completed validated questionnaires of the Psychosocial Safety Climate (PSC-12) scale, short version Demand Induced Strain Compensation (DISQ 2.1), Oldenburg Burnout Inventory - Emotional Exhaustion domain and the Three Eating Factor Questionnaire (TEFQ) -Uncontrolled Eating domain; assessing psychosocial safety climate, job demands and job resources, emotional exhaustion, and uncontrolled eating behaviour, respectively. Body mass index (BMI) was calculated based on weight and height. The research statistical model was tested by two-steps of assessment replicating partial least squares structural equation modelling (PLS-SEM). Result: The results show that psychosocial stressors (psychosocial safety climate, job demands and job resources) had significant effects on emotional exhaustion (β= -0.149, p=0.004; β= 0.223, p<0.001; β= -0.127, p=0.013). Emotional exhaustion predicted by work stressors may act as a chain reaction which could result in uncontrolled eating (β=0.138, p=0.005) and high BMI (β=0.185, p<0.001). Emotional exhaustion does mediate the relationship between PSC and uncontrolled eating behaviour (β= -0.021 [95% boot CI bias corrected: -0.048, -0.002]). Conclusion: The psychosocial stressors at work are significant factors for emotional exhaustion, which further signifies the positive effect on uncontrolled eating behaviour and BMI among Malaysian male employees.

Background: COVID-19 was first detected in Malaysia on 25 January 2020. Multiple clusters were detected in Petaling District, with the first locally transmitted case reported on 8 February. Descriptive analyses of the epidemiology of the COVID-19 outbreak in Petaling are presented, from the first case to the end of the first wave. Methods: All laboratory-confirmed COVID-19 cases reported to the Petaling District Health Office between 1 February and 26 June 2020 were analysed. Socio-demographic characteristics, symptoms, date of onset, date of exposure, travel history and history of comorbidities were obtained by phone interviews using one of two investigation forms. The descriptive analysis was conducted according to time, place and person. Results: There were 437 COVID-19 cases, for an incidence rate of 24/100 000 population. Ten (2.3%) deaths and 427 recovered cases were recorded. Of the 437 cases, 35.5% remained asymptomatic and 64.5% were symptomatic. Common symptoms included fever (43.8%), cough (31.6%) and sore throat (16.2%); 67.3% had no comorbidities, 62.5% reported close contact with a confirmed case, and 76.7% were local infections. Transmission occurred in four main groups: religious gatherings (20.4%), corporations (15.1%), health facilities (10.3%) and a wholesale wet market (6.4%). In 31.9% of confirmed cases, an epidemiological link to an asymptomatic case was found. Conclusion: Transmission of the disease by asymptomatic cases should be emphasized to ensure continuous wearing of face masks, hand hygiene and social distancing. Further research should be conducted to better understand the transmission of SARS-CoV-2 from asymptomatic cases.

The application of artificial intelligence (AI) is on the rise in the healthcare industry. However, the study on the physicians’ perspectives is still lacking. The study aimed to examine physicians’ attitudes, expectations, and concerns regarding the application of AI in medicine. A cross-sectional study was conducted in October 2019 among physicians in a tertiary teaching hospital in Malaysia. The survey used a validated questionnaire from the literature, which covered: (1) socio-demographic profile; (2) attitude towards the application of AI; (3) expected application in medicine; and (4) possible risks of using AI. Comparison of the mean score between the groups using a t-test or one-way analysis of variance (ANOVA). A total of 112 physicians participated in the study: 64.3% from the clinical departments; 35.7% from the non-clinical specialties. The physicians from non-clinical departments had significantly higher mean attitude score (mean = 14.94 ± 3.12) compared to the clinical (person-oriented) departments (mean = 14.13 ± 3.10) and clinical (technique-oriented) departments (mean = 13.06 ± 2.88) (p = 0.033). The tech-savvy participants had a significantly higher mean attitude score (mean = 14.72 ± 3.55) than the non–tech-savvy participants (mean = 13.21 ± 2.46) (p = 0.01). There are differences in the expectations among the respondents and some concerns exist especially on the legal aspect of AI application in medicine. Proper training and orientation should precede its implementation and must be appropriate to the physicians’ needs for its utilization and sustainability.