The COVID-19 pandemic potentially increases doctors’ work demands and limits their recovery opportunity; this consequently puts them at a high risk of adverse mental health impacts. This study aims to estimate the level of doctors’ fatigue, recovery, depression, anxiety, and stress, and exploring their association with work demands and recovery experiences. This was a cross-sectional study among all medical doctors working at all government health facilities in Selangor, Malaysia. Data were collected in May 2020 immediately following the COVID-19 contagion peak in Malaysia by using self-reported questionnaires through an online medium. The total participants were 1050 doctors. The majority of participants were non-resident non-specialist medical officers (55.7%) and work in the hospital setting (76.3%). The highest magnitude of work demands was mental demand (M = 7.54, SD = 1.998) while the lowest magnitude of recovery experiences was detachment (M = 9.22, SD = 5.043). Participants reported a higher acute fatigue level (M = 63.33, SD = 19.025) than chronic fatigue (M = 49.37, SD = 24.473) and intershift recovery (M = 49.97, SD = 19.480). The majority of them had no depression (69.0%), no anxiety (70.3%), and no stress (76.5%). Higher work demands and lower recovery experiences were generally associated with adverse mental health. For instance, emotional demands were positively associated with acute fatigue (adj. b = 2.73), chronic fatigue (adj. b = 3.64), depression (adj. b = 0.57), anxiety (adj. b = 0.47), and stress (adj. b = 0.64), while relaxation experiences were negatively associated with acute fatigue (adj. b = −0.53), chronic fatigue (adj. b = −0.53), depression (adj. b = −0.14), anxiety (adj. b = −0.11), and stress (adj. b = −0.15). However, higher detachment experience was associated with multiple mental health parameters in the opposite of the expected direction such as higher level of chronic fatigue (adj. b = 0.74), depression (adj. b = 0.15), anxiety (adj. b = 0.11), and stress (adj. b = 0.11), and lower level of intershift recovery (adj. b = −0.21). In conclusion, work demands generally worsen, while recovery experiences protect mental health during the COVID-19 pandemic with the caveat of the role of detachment experiences.

Background: Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Artificial intelligence (AI) holds significant potential roles in enhancing the detection of lung nodules through chest X-ray (CXR), enabling earlier diagnosis and improved outcomes. Methods: Papers were identified through a comprehensive search of the Web of Science (WOS), Scopus, and Ovid Medline databases for publications dated between 2020 and 2024. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 34 studies that met the inclusion criteria were selected for quality assessment and data extraction. Results: AI demonstrated sensitivity rates of 56.4–95.7% and specificities of 71.9–97.5%, with the area under the receiver operating characteristic (AUROC) values between 0.89 and 0.99, compared to radiologists’ mean area under the curve (AUC) of 0.81. AI performed better with larger nodules (>2 cm) and solid nodules, showing higher AUC values for calcified (0.71) compared to non-calcified nodules (0.55). Performance was lower in hilar areas (30%) and lower lung fields (43.8%). A combined AI-radiologist approach improved overall detection rates, particularly benefiting less experienced readers; however, AI showed limitations in detecting ground-glass opacities (GGOs). Conclusions: AI shows promise as a supplementary tool for radiologists in lung nodule detection. However, the variability in AI results across studies highlights the need for standardized assessment methods and diverse datasets for model training. Future studies should focus on developing more precise and applicable algorithms while evaluating the effectiveness and cost-efficiency of AI in lung cancer screening interventions.

Driving is the most prevalent form of commuting for most workers but is also perhaps the most hazardous mode of travel with unsafe driving contributing significantly to road traffic accidents. Despite nurses having been reported as being at higher risk of commuter-related accidents over the last three decades, little is known about unsafe driving behaviours among nurses while commuting, which is unique from other driving routines. Additionally, the lack of appropriate tools to measure such behaviours is apparent. This study aims i) to identify unsafe driving behaviours among nurses while commuting and ii) to develop a scale to assess nurses' unsafe commuting driving behaviours. The study employed a multiphase and multimethod approach to develop the scale, which was subject to stringent validation and evaluation. Themes were specified via the Nominal Group Technique (NGT). Six themes were identified namely: i) violations and reckless driving, ii) negative emotions, iii) drowsy driving iv) mind wandering, v) error and vi) carelessness. Content and face validity were sought through expert review. A total of 442 nurses' data were collected across multisite hospitals for evaluation. Exploratory factor analysis (EFA) resulted in recovered structure and was confirmed through Confirmatory Factor Analysis (CFA) with structural equation analyses being conducted to test predictive validity. All constructs met adequate validity and reliability. Nurses' unsafe driving behaviours while commuting were identified with a novel scale to assess them being both developed and validated. The resulting MyUDWC scale is a suitable tool for measuring nurses' unsafe driving behaviours while commuting.

Unsafe driving behaviour is associated with the risk of crashes. Although commuting crashes prevail among healthcare workers (HCWs), unsafe driving behaviour during daily commutes remains unexplored in this group. The aim of our study was therefore to address this gap and to clarify the concept of unsafe driving behaviour among HCWs while commuting. To do that, we ran literature search in Medline, CINAHL, Scopus, and Web of Science and selected appropriate articles following the scoping review procedure, while data extraction and analysis followed the procedure for concept analysis. A total of 46 published studies met inclusion criteria. Most were from the USA (n=30), predominantly involved medical doctors (n=21), and were cross-sectional (n=24) in design. Concept analysis identified four properties of unsafe driving behaviour: 1) pressure and negative emotion, 2) drowsy driving, 3) risky driving and rules violation, and 4) distraction/inattention. Work scheduling factors emerged as the most frequently reported antecedents, while crashes were the most reported consequences. By identifying the core elements of unsafe driving behaviour among HCWs this study proposes a conceptual framework to guide future research and interventions. This framework can serve as a valuable resource for policymakers and researchers, enabling them to develop targeted strategies to address unsafe driving behaviour of HCWs during commuting, with the ultimate goal to reduce the associated crash risks.

Background: Universal Health Coverage (UHC) is a global imperative to ensure equitable access and financial protection. While many nations utilize health benefit packages (HBPs) to define these entitlements, the transition from implicit to explicit prioritization remains a significant policy challenge. This study explored the perspectives of Malaysian healthcare stakeholders regarding the potential architecture, guiding principles, and operational modalities of an explicit National HBP (NHBP) within the context of national health reform. Methods: We employed a participatory, multi-method qualitative design utilizing a modified Policy Delphi framework inspired by Evidence-Informed Deliberative Processes (EDPs). The study was executed in two phases: (1) semi-structured key informant interviews ( n = 7) to identify core system bottlenecks, followed by (2) a dual-arm Policy Delphi workshop ( n = 84) integrating baseline evidence briefs with deliberative foresight tools. To enhance deliberative rigour, specialized techniques including Problem Mapping, Futures Wheel, and Fishbowl discussions were utilized. Data were analysed using a thematic triangulation approach to ensure the inclusivity and robustness of the resulting framework. Results: Triangulation revealed seven interdependent system domains essential for NHBP implementation: a robust governance model, a sustainable financing architecture, the central role of health technology assessment (HTA), a phased implementation pace, an equity-centred approach, a defined system scope and a transparent framework with legal anchoring. Furthermore, nine priority-setting criteria were identified: clinical effectiveness, cost-effectiveness, safety, budget impact, feasibility, strategic alignment, equity, burden of disease, and social acceptability. Stakeholders emphasized that an explicit NHBP must transcend technocratic design, requiring social legitimacy and statutory protection to be sustainable. Conclusions: This study presents a stakeholder-driven, evidence-informed framework and implementation roadmap for an explicit NHBP in Malaysia, moving beyond traditional top-down policy design. By utilizing deliberative foresight techniques, the research offers an actionable roadmap that aligns Malaysia’s UHC journey with systemic reform priorities.