Work-related musculoskeletal disorders (WMSDs) and psychological stress remain major occupational health challenges among teaching professionals in adult education, yet their interconnected causes are often underexplored. This narrative review aims to identify multifactorial risk factors that contribute to these conditions and to propose a comprehensive framework that enhances understanding of teaching professionals’ well-being. A systematic synthesis of recent epidemiological and occupational health studies was conducted to analyse both immediate and underlying determinants across human, workplace, organisational, and socioeconomic dimensions. The findings reveal that more than two-thirds of teaching professionals experience WMSDs, particularly in the neck and lower back, while psychological stress affects over seventy percent globally. The combined effects of poor ergonomics, prolonged static postures, excessive workload, and limited organisational support contribute significantly to both physical and psychological strain. Broader contextual influences such as job insecurity, insufficient institutional resources, and societal undervaluation further intensify these risks. The review identifies a reciprocal relationship between physical discomfort and psychological distress, where each condition amplifies the other through behavioural and physiological mechanisms. The proposed integrative framework establishes a foundation for targeted interventions and evidence-based policy, promoting a shift toward holistic, system-oriented approaches to occupational health for teaching professionals in professional education settings.

The objectives of this study are to assess the traffic noise levels in Shah Alam, Selangor, Malaysia, and to propose traffic noise model among influential factors. Five factors and 20 attributes were measured at three locations using a sound level meter and analyzed using the PLS-SEM approach. The results of this study showed that high traffic noise levels on urban roads exceeded the maximum permissible limit of 60 dBA during the day, according to Malaysian guidelines. However, the integrated traffic noise prediction model approach has specific indirect effects: built environment -> climate conditions -> traffic flow.

The country's transport infrastructure is a key element of Malaysia's economic growth and achievement of its 2020 development goal. There will be a lot of expansion, including residential, industrial, and commercial businesses with high demand for transport, which might lead to more vehicles on the road, more traffic jams, and more noise pollution from traffic. Therefore, this study aims to compare the level of noise between residential and industrial areas as well as to analyse traffic noise attribute factors spatially and geographically, such as road geometry, traffic flow and climate conditions. The measurement was conducted at Shah Alam, the capital city of Selangor, Malaysia with each three sampling point locations in residential and industrial areas. The noise level was measured using a Sound Level Meter (SLM) with intervals of 3 hours from morning (8.00-11.00 a.m.), afternoon (12.00-3.00 p.m.) and evening (4.00-7.00 p.m.). The total sampling measurement was 36 for traffic noise. Other attribute factors of traffic noise were documented simultaneously, such as using a manual counter to measure the traffic volume, including buses, lorries, vans, motorcycles and cars. A tap meter was utilised to measure the width of the road for road geometry. However, climate conditions were obtained from secondary data (Google Weather Database). The result shows that the traffic noise level was above the standard regulation set by the Department of Environment, Malaysia, for Suburban and Urban Residential (Medium and Density) categories >65 dBA and Industrial areas > 75 dBA. Traffic noise attributes such as road geometry, traffic flow and climate conditions also contributed significantly to the high noise level. This work may have several implications for reference standards for measuring, calculating, and evaluating noise.