Journal of Health and Safety at Work

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Introduction: The healthcare system is one of the largest sectors in most countries and is a socio-technical system in which people play a preponderant role. Nowadays medical work systems are facing three major challenges: 1) Healthcare costs, 2) Quality and patient demands and 3) complexity of healthcare. These problems show the necessity of applying ergonomic models in the healthcare sector. The aim of this study was to review the practical ergonomic models in healthcare system.
 

Material and method: For this review article, the authors searched through ScienceDirect, PubMed, ProQuest internet databases from 2005-2014 using the following keywords: Healthcare, Ergonomics, Human factors and model.
 

Result: Overall, 85 articles were reviewed. By evaluating articles' titles, 30 articles related to the study subject were chosen. Then, reviewing the abstracts resulted in 15 articles and in the final step 5 full-text articles were selected which described practical models of ergonomics in healthcare: 1) SEIPS, 2) DIAL-F, 3) Extended Patient-Staff-Machine-Interaction, 4) Adapted Medical-Task and 5) Recursive Hierarchical Task-Process-Task-Model.
 

Conclusion: Most of the published studies emphasize on application of ergonomic models in healthcare centers since these models may reduce their problems. These ergonomics approaches support patient-centered treatment processes, user-oriented design of medical environments, efficient utilization of resources and increase motivation of clinical staff.

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Introduction: Affinity for Technology Interaction (ATI) refers to the users’ tendency to actively interact with a digital system. ATI is a personal characteristic affecting many aspects of human-technology interaction. The present study aimed to assess the psychometric properties of the Persian version of the ATI scale.
Material and Methods: The Persian version of the scale was developed in accordance with the forward-backward translation approach. The construct validity of the scale was assessed using exploratory and confirmatory factor analyses. The correlation of the scale with the Big-five personality traits, need for cognition (NFC), age, gender, and field of study was also investigated.
Results: In contrast to the original scale, the Persian ATI had two components, confirmed by the exploratory and confirmatory factor analyses. The first component contained eight items and the second contained one item, item 3. So, this item was removed from the scale. The 8-item scale demonstrated excellent reliability (coefficient=0.90). The Persian ATI was not significantly correlated with extraversion, agreeableness, conscientiousness, and neuroticism (p>0.05). In contrast, it was significantly correlated with openness to experience (0.175, p<0.01) and NFC (0.36, p<0.01). The Persian ATI score for men was higher than for women. Various age groups were not significantly different in terms of the Persian ATI score.
Conclusion: In contrast to the original version, the Persian version of ATI is composed of eight items. Other characteristics, including unidimensionality and correlation with other personality traits, are similar to the original version.

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Introduction: Fumigant gases in maritime and container chains, along with occupational noise in marine and manufacturing industries, are among the most significant chronic risk factors. They are usually assessed separately, despite their simultaneous impact on workers’ health. The importance of this study lies in presenting an integrated approach for real-time monitoring of combined risk and aligning it with occupational exposure limits (OELs). The aim is to develop and validate an interpretable, regulation-oriented framework for predicting combined risk.
Material and Methods: This research integrated and normalized data from the Global Burden of Disease (GBD) 2021 study including age-standardized disability rates (ASDR) and average annual percentage change (AAPC) for 204 countries with occupational exposure limit tables for fumigants. A Sugeno-type fuzzy inference system with three inputs and four rules was designed. Weights and membership function boundaries were optimized using the Prairie Dog Optimization algorithm, and a threshold-based scenario generation module was applied to produce high-risk synthetic data. Model performance was evaluated through an OEL compliance test.
Results: Findings revealed that the proposed optimization reduced the loss function by 42% compared to random search. The mean absolute error (0.028 ± 0.006) and root mean square error (0.041) were obtained. Threshold-based scenario generation improved data coverage in high-risk regions from 0.62 to 0.90 and increased the accuracy of critical condition detection from 0.71 to 0.89. The OEL compliance index reached 0.93, confirming input weighting as the most influential factor.
Conclusion: The proposed framework simultaneously ensures numerical accuracy, interpretability, and regulatory compliance with occupational exposure limits. It can be deployed within real-time monitoring dashboards for ports and factories. Future research should integrate IoT sensors and multi-objective optimization to enable dynamic updates in response to evolving regulations and operational conditions.