BackgroundAutomation, including both robots and AI, are increasingly implemented in the public sector to handle an increased demand for services while facing a shortage of staff. However, little is known of how automation is related to employees' work characteristics and well-being.ObjectiveThe purpose of the present study was to examine how automation is related to work characteristics and well-being in a national sample of social workers.MethodsA cross-sectional survey was conducted with a sample of 1428 social workers in Sweden, comparing those working with automation technologies (e.g., robotization in terms of automated decision-making support in financial aid) to those without.ResultsThe findings indicate that automation is negatively related to relational aspects of work, such as beneficiary contact and social support, and increases perceived workload and role conflict. However, no significant differences were found on decision-making autonomy, role clarity, or skill variety. Despite these work design changes, no significant differences in well-being outcomes, such as burnout and vigor, were observed between the two groups.ConclusionThe findings suggest that automation may alter the nature of social work without necessarily changing employee well-being. These insights have implications for organizations seeking to implement automation in a way that supports employees' work experience and well-being.

BackgroundOccupational Health Check-ups (OHCs) have evolved from identifying work-related health risks to supporting sustainable employability through individualized lifestyle advice, but translating multidimensional demographic, occupational, and health characteristics into effective, personalized recommendations remains complex and lacks validated decision-support methods.ObjectiveAims of this study are to: 1) develop a Machine Learning Decision Support Tool (ML-DST) based on workers' demographic, occupational, and health characteristics to support personalized advice about lifestyle changes to improve sustainable employability, and 2) evaluate performance of the ML-DST.MethodsReal-world OHC data from a population-based cohort (2010-2020) were used to develop ML-DST models providing lifestyle advice (physical activity, smoking, alcohol, nutrition, relaxation, or no change) to improve workability and vitality. Paired baseline and 1-4-year follow-up data were used to train and evaluate 12 models. Per model, three rule-based algorithms were applied. Performance was considered useful when both True Positive Rate (TPR) and macroF1 score were ≥0.7. The best performing algorithm is proposed per model for inclusion in the final ML-DST.ResultsOf 20,440 individual OHCs, 3412 paired sets were included. Best TPRs ranged from 0.51 to 0.71. One model achieved TPR and macroF1 scores ≥0.7, supporting nutritional behavior change to improve vitality; however, its profiles were not conceptually meaningful. The other 11 models did not achieve useful quantitative performance.ConclusionTraining algorithms for an ML-DST to support advice about lifestyle changes to improve long-term sustainable employability using data from regular Occupational Health Check-ups did not lead to useful model performance for improving workability and vitality.

BackgroundEmerging artificial intelligence-based technologies are increasingly influencing teaching and learning practices in healthcare education, offering new opportunities for student engagement, learning, and skill development.ObjectiveThe purpose of this study was to explore utilization of artificial intelligence in audiology and speech-language pathology education based on insights from students. Further, to explore their awareness, use, perceived benefits, barriers, and attitudes toward the integration of artificial intelligence.MethodA cross-sectional online survey of 17 questions was distributed to 483 audiology and speech-language pathology students enrolled in undergraduate, postgraduate and doctoral programmes across multiple institutions in India through social media platforms. The questionnaire was developed following a review of previously published studies examining students' perceptions of the implications of artificial intelligence in education. Descriptive analysis was used to analyse the findings.ResultsA total of 286 students participated in the study (mean age = 21.73 ± 2.33 years), of whom 82.2% were females. Most participants (96.9%) were aware of artificial intelligence technologies, and 89.9% indicated using artificial intelligence tools for educational purposes. Regarding the future role of artificial intelligence in education, 75.1% disagreed that it could replace human lecturers. Students primarily used it for assignments and exam preparation, while limited institutional acceptance was a major barrier to its implementation.ConclusionsStudents expressed an optimistic view regarding the application of artificial intelligence in education. Students reported concerns about inaccurate artificial intelligence-generated information, while limited acceptance, inadequate knowledge, and ethical concerns hindered its effective integration into education.

BackgroundArtificial intelligence (AI) is spreading through health care, yet systems that administer assessments directly to clients remain untested in occupational therapy (OT), where the initial assessment both gathers data and builds therapeutic rapport.ObjectiveTo compare user experience of a purpose-built, remote AI-administered OT initial assessment with a human-administered assessment.MethodsThirty adults completed an AI-administered and a human-administered OT initial assessment in randomized order one to two weeks apart (crossover design), rating usability, perceived compassion, goal setting and satisfaction after each and stating an overall preference; 28 gave reflection interviews. Formats were compared within participants (Wilcoxon signed-rank, McNemar, Holm-corrected) and interviews analyzed thematically.ResultsParticipants were randomized 15 per sequence; order affected no outcome. AI usability was high (modified System Usability Scale 81.1). Perceived compassion favored the human format (median 9.90 vs 7.53; Hodges-Lehmann difference 2.39, 95% CI 1.56-3.11; p < 0.001), as did satisfaction (all p < 0.001). Agreement that goals were clear, personalized, relevant, attainable and motivating did not differ (all p ≥ 0.25), but strong agreement was more frequent after the human format on four of five items (40.0-53.3 percentage points, all p ≤ 0.002). Overall, 83% (25/30) preferred the human format. Four themes were identified.ConclusionsThe AI-administered assessment was highly usable and produced goals accepted as clear, relevant and attainable at rates matching the human format; what differed was the depth of endorsement, perceived compassion and preference. Conversational AI appears better placed to supplement therapist-led assessment than to replace it.

BackgroundThe rapid adoption of artificial intelligence (AI) in the information technology (IT) reshaping the work design, with significant consequences for employee well-being and ergonomics. While AI enhances efficiency through task automation, its influence on workload dynamics and ergonomic consequences remains not sufficiently understood, particularly in emerging economy contexts.ObjectiveGrounded in the Job Demands-Resources (JD-R) framework and informed by Sociotechnical Systems Theory, this study investigates the relationship between AI adoption and employees' physical and cognitive ergonomic outcomes while examining the mediating roles of trust in AI and workload among information technology professionals in India.MethodsTo assess this, seven reflective constructs were measured. The AI adoption construct has three sun-dimensions: performance expectancy, effort expectancy and usage behavior/intention. Employee ergonomics has two sub-dimensions: physical ergonomics discomfort, cognitive ergonomics/digital fatigue, which are modeled as higher-order constructs. However, trust and workload dynamics are modeled as lower-order constructs. The higher-order constructs were assessed along with the lower-order constructs. Exploratory and confirmatory factor analyses were carried out to unravel the structural relationships among the constructs, and hypothesis testing was performed using structural equation modeling.ResultsThe findings indicate that AI adoption significantly enhances trust in AI and reduces workload, Trust in AI is negatively associated with workload, while workload positively influences both physical ergonomic discomfort and cognitive fatigue. AI adoption also demonstrates a significant direct effect on employee ergonomics. The mediation analysis revealed that trust and workload fully mediated the relationship between AI adoption and ergonomics, whereas trust partially mediates the relationship between AI adoption and workload. The serial mediation results reveal partial mediation on role of trust and workload in the nexus among AI adoption and ergonomics.ConclusionConsistent with the Job Demands-Resources (JD-R) framework, the findings demonstrate that AI functions simultaneously as a job resource and a source of new job demands. Trust in AI serves as an important psychological resource that reduces workload and enhances employees' physical and cognitive ergonomic outcomes. Trust in AI emerges as a vital psychological mechanism that mitigates workload and enhances ergonomic outcomes. The outcome contributes to the development of human-centric AI implementation strategies by highlighting the importance of trust-building and workload management in designing sustainable, ergonomically sound workplaces.

BackgroundManual load-carrying remains common in developing countries and poses significant occupational health risks. However, integrated three-dimensional (frequency, severity, and interference)-based ergonomic assessment of the head porterage job is limited.ObjectiveThis study assesses the distribution of Musculoskeletal Discomfort (MSD) among head porters in selected open-air markets in Ibadan, Nigeria.MethodsA descriptive cross-sectional survey was done among 100 head-porters in Bodija, Oja-Oba, and Challenge markets using stratified and purposive sampling. Data were collected via an adapted Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) and analysed by descriptive statistics. CMDQ scores for frequency, severity, and work interference were multiplied to obtain composite CMDQ index for each body region, while percentage CMDQ index (%CMDQ) was calculated to rank affected body regions. Data were analysed using chi-square tests and Spearman's rank correlation at a significance level of 0.05.ResultsMost participants were females (78.0%), aged 30-55 years (71.0%), and worked more than six hours per day (79.0%). MSD was widely reported across multiple body regions, with the lower back being the most affected, followed by the left thigh, right thigh, neck, and knees, with %CMDQ of 8.8, 7.8, 7.7, 7.6, and 7.2%, respectively. MSD frequently interfered with work performance and reduced job satisfaction. A weak, but statistically significant, positive correlation (r = 0.31, p < 0.05) was found between MSD severity and daily working hours.ConclusionsThe multidimensional approach provides a comparative empirical framework for prioritising MSD severity across body regions. The high ergonomic risk observed highlights urgent need for targeted interventions in the informal market environments.

BackgroundAlthough individuals who experience spinal cord injury (SCI) face significant challenges in entering and sustaining employment, there is limited evidence on how psychological factors shape their employment decisions and those of potential employers.ObjectiveThis qualitative study aimed to identify emotions, thoughts, and motives of persons who experience SCI and employers that influence their decision to return to or continue competitive employment.MethodsWe conducted problem-centered interviews with ten individuals who experience SCI and ten employers who had recently been involved in a vocational integration process at the Swiss Paraplegic Centre. The interviews were analyzed using thematic analysis, and the findings validated through a focus group discussion with vocational integration experts.ResultsWork performance and related uncertainties about what it means to work with an SCI emerged as central themes for both participant groups. In addition, persons who experience SCI emphasized the importance of their job content and interpersonal work environment, whereas employers highlighted the critical role of human resource planning and the injured employees' work attitude.ConclusionsThe themes identified in this study are central to the return-to-work and employment decision-making process following the onset of an SCI. They can inform the development of guidelines for vocational integration practice, support open communication between persons with SCI and their employers, and guide awareness campaigns aimed at promoting inclusive national labor markets.

BackgroundFacilitating a worker's return to work (RTW) following disability helps to prevent prolonged disability. While stakeholder trust is crucial for RTW success, its constituents remain empirically underexplored.ObjectiveThis study aims to explore the constituents of trust from RTW coordinators' perspective.MethodWe took inspiration from the critical incident technique and conducted exploratory interviews with RTW coordinators from the healthcare, insurance, and workplace sectors with at least three years of experience helping workers RTW following disability due to musculoskeletal or common mental disorders. Interviews were transcribed and underwent theory-informed deductive thematic analysis so inductive insights could emerge.ResultsMost of the eleven RTWCs interviewed were insurer representatives (n = 4) or workplace representatives (n = 5). Their narratives confirmed previously documented constituents of trust and revealed new subconstituents. Notably, serenity-a trustee characteristic-was determined to be an antecedent of trust alongside several contextual factors. Furthermore, the attribute risk of opportunism was refined to emphasize the trustor's perception of the risk, and willingness to act, previously a consequence of trust, was recategorized as an attribute. These findings suggest there exist other elements that could be leveraged to strengthen trust in RTW practices and highlight relational consequences that may influence organizational culture.ConclusionSince empirical analyses of trust constituents in RTW contexts are, to our knowledge, largely absent from the literature, this study provides an original contribution on this subject. Targeting antecedents of trust could help build trust and collaboration among stakeholders, thereby strategically strengthening these factors.

BackgroundThe use of artificial intelligence (AI) in workplace disability contexts is expanding, raising important ethical concerns. Existing frameworks, such as the OECD Framework for the Classification of AI Systems, provide structure for understanding AI applications but lack an explicit focus on ethics in relation to disability.ObjectiveThis systematic review examines ethical issues arising from the use of AI for employees with disabilities and aims to interpret the OECD framework through an explicit ethics lens.MethodsWe conducted a systematic review of 23 peer-reviewed studies identified through PRISMA-guided searches. Using the OECD AI framework as a foundation, we developed an extended multidimensional framework consisting of five interconnected domains: (1) Context, (2) People, (3) Data & Input, (4) AI Model, and (5) Task & Output and analyzed through the lens of ethics.ResultsFour recurring ethical concerns were identified: (1) automation bias, (2) data privacy and informed consent, (3) discrimination and inequitable access to opportunities, and (4) AI governance gaps. Ethical risks were found to be context-dependent, disproportionately affecting disabled workers, and exacerbated by biased or incomplete data and opaque AI models.ConclusionsThis review provides a structured synthesis of current evidence and highlights the need for responsible and inclusive AI practices. We propose key directions for research and practice, including algorithmic audits, inclusive testing, transparent data practices, interdisciplinary oversight, and worker-centered governance. Integrating ethics throughout a multidimensional framework offers a roadmap for advancing equitable AI in workplace disability contexts.

BackgroundArtificial intelligence (AI) is rapidly transforming healthcare delivery across multiple disciplines. Allied health professions-including diagnostic medical sonography (DMS), health informatics (HI), medical billing and coding (MBC), midwifery (MW), occupational therapy (OT), physical therapy (PT), and physician assistant (PA) practice-represent critical yet understudied domains of AI integration. Understanding of how AI technologies are adopted across these professions is essential for supporting ethical and effective implementation.ObjectiveThis review synthesizes the current literature on AI integration across seven allied health professions, examining applications in education, research, and clinical practice while identifying implementation challenges, and ethical considerations.MethodsA structured literature review of peer-reviewed articles, systematic reviews, and professional practice guidelines published between 2018 and 2025 was conducted. Articles were selected based on relevance to AI applications in allied health professions and discussion of educational, research, or clinical implementations. Particular attention was given to applications where AI augments rather than replaces clinical judgment and therapeutic relationships.ResultsAI integration demonstrates several cross-disciplinary trends. In education, simulation platforms, adaptive learning systems, and virtual patient support competency development and personalized instruction. In research, AI enhances large-scale data analysis, predictive modeling, and evidence synthesis. Clinically, AI improves diagnostic accuracy, workflow efficiency, and decision support across professions. However, implementation raises concerns regarding data privacy, algorithmic bias, automation complacency, and inequitable access to technology.ConclusionsAI is reshaping allied health education, research, and clinical practice. Responsible integration requires discipline-specific validation, ethical governance, and interprofessional collaboration to ensure AI strengthens- rather than replaces-professional expertise and patient-centered care.

BackgroundAthletic professionals (athletic trainers and physical therapists) perform injury prevention, health management, evaluation, emergency care, post-injury protection, rehabilitation, and administrative duties. Their focus on athletes' health may lead them to overlook high physical and psychosocial demands that may increase musculoskeletal pain (MSP), fatigue, and unhealthy dietary habits.ObjectiveTo examine MSP, fatigue, and dietary habits among athletic professionals who provide on-site team support and explore how fatigue and dietary habits relate to MSP.MethodsParticipants were required to be athletic professionals providing on-site team support. This cross-sectional online survey assessed 3-month work-related MSP in body regions, fatigue using a modified Fatigue Assessment Scale, and dietary habits using a structured food-frequency questionnaire. Logistic regression and mediation analyses examined associations and indirect associations, adjusting for relevant confounders.ResultsA total of 132 valid responses were included. MSP was highly prevalent, particularly in the lower back (83%), wrists/fingers (80%), and neck (67%). Fatigue (OR = 3.1, 95% CI = 1.34-7.45) and several suboptimal dietary habits (e.g., irregular meal timing, inadequate intake, fast eating, unbalanced diet, replacing meals with snacks, emotional eating) were significantly associated with MSP. Exploratory mediation analyses indicated statistically significant indirect associations via inadequate intake, replacing meals with snacks, and emotional eating, suggesting these behaviours may partially account for the fatigue-MSP association.ConclusionsAthletic professionals experience a high burden of MSP, fatigue, and suboptimal dietary habits that may be interrelated in ways that exacerbate work-related health. Workplace strategies should address workload, fatigue management, and access to healthy eating options to support sustainable practice and well-being.

BackgroundHigh turnover and emigration among healthcare professionals have raised serious concerns about workforce stability and the quality of patient care in Turkey.ObjectiveThis cross-sectional study investigated the relationships among job satisfaction, burnout, turnover intentions, and the desire to work abroad.MethodsSurvey data were collected from 336 doctors and nurses and analyzed using structural equation modeling.ResultsFindings indicate that job satisfaction is negatively associated with intent to leave (β = -0.24, p < 0.001), whereas burnout was positively associated with intent to leave (β = 0.59, p < 0.001). Burnout partially mediated the relationship between job satisfaction and intent to leave (indirect β = -0.35, p < 0.001). Job satisfaction and burnout together explained 57% of the variance in intent to leave, while intent to leave explained 28% of the variance in desire to go abroad. Demographic analyses showed that younger doctors reported higher burnout, and single nurses reported a higher desire to work abroad and intent to leave.ConclusionThese findings underscore the need for enhanced workplace conditions, career development opportunities, and effective strategies to mitigate burnout and retain qualified healthcare professionals, thereby stabilizing the healthcare system.

BackgroundInadequate or misleading health information about silicosis can delay diagnosis, reporting, and exposure removal, potentially affecting risk communication and timely occupational health interventions.ObjectiveThis study evaluates the reliability, quality, and readability of Large Language Models (LLMs) including ChatGPT (GPT-5), Gemini (2.5 Flash), and Copilot (Smart GPT-5), in answering patient-oriented silicosis questions, focusing on risk communication rather than clinical decisions.MethodsSilicosis-related questions were collected from online forums (September 1-7, 2025), and 25 frequently asked, clinically relevant questions were selected and submitted to the LLMs (September 8-14, 2025). Two occupational medicine specialists/pulmonologists independently evaluated responses (September 15-30, 2025) for reliability, quality, and readability using modified DISCERN, Global Quality Scale, and readability indices. Analyses used SPSS version 31.0.ResultsThe LLM-generated responses demonstrated fair reliability. Median (min-max) scores were 23 (12-27) for ChatGPT, 21.5 (14-29) for Gemini, and 26 (17.5-31) for Copilot. Copilot scored significantly higher than ChatGPT and Gemini (p = 0.014). GQS scores were similar across models (p = 0.102), with median scores of 4 (3-5) for ChatGPT and Copilot and 4 (2-5) for Gemini. Readability levels corresponded predominantly to secondary or tertiary education, with no significant differences between the models (all p > 0.05).ConclusionLLMs may enhance worker awareness but have limitations in accuracy and readability. They should complement, not replace, expert-supervised occupational health services. Future studies should align LLMs with international standards, improve accessibility, and establish governance frameworks for transparent and equitable AI use in occupational health.

BackgroundOccupational accidents in open-pit mining remain a significant workplace safety and public health concern due to hazardous environmental conditions, high-risk tasks, and complex human-organizational interactions. Data-driven risk modeling may enhance accident prevention and occupational health management.ObjectiveTo identify key determinants of occupational accidents in an open-pit coal mining operation and develop a predictive risk assessment model to support injury prevention and safety decision-making.MethodsA retrospective quantitative study was conducted using accident records (2017-2024) from a large open-pit mining company. Variables included accident timing, location, department, job position, age, work experience, unsafe acts, unsafe conditions, personal factors, and job-related factors. Multivariate regression and artificial neural network (ANN) modeling were applied to examine associations and predict the number of affected workers. Model performance was evaluated using correlation coefficients, mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and bias.ResultsUnsafe acts, hazardous environments, low worker motivation, and inadequate supervision were dominant risk contributors. Regression analysis showed significant associations between accident variables and injury occurrence (p < 0.05). The ANN model demonstrated strong predictive performance (overall R = 0.973; testing R = 0.992) with low error (MSE = 0.015; MAPE = 13.2%), while the linear regression model explained comparatively little variance (R[2] = 0.094), indicating that occupational risk in this setting is driven predominantly by nonlinear interactions among behavioral, environmental, and organizational factors. Accidents were more frequent among operators, workers aged 26-30 years, and employees with >5 years of experience.ConclusionsIntegrating statistical and machine learning approaches provides a structured framework for identifying occupational risk patterns. Predictive modeling can support targeted supervision, behavioral safety interventions, and hazard control strategies in high-risk industrial settings.

BackgroundPhysiotherapy technicians are frequently exposed to heavy workloads and high patient volumes, which can lead to physical and emotional strain. Work-related musculoskeletal disorders (WMSDs) and burnout are common among healthcare workers and may negatively impact job satisfaction and overall occupational health. Data specifically addressing physiotherapy technicians, however, remain limited.ObjectiveTo examine the prevalence of WMSDs, burnout, and job satisfaction among physiotherapy technicians working in public and private hospitals.MethodsThis observational study was conducted between April 2025 and March 2026. A total of 122 physiotherapy technicians employed in public and private hospitals in Istanbul were included. Demographic data were recorded. The Extended Nordic Musculoskeletal Questionnaire assessed musculoskeletal disorders, the Maslach Burnout Inventory evaluated burnout, and the Minnesota Satisfaction Questionnaire measured job satisfaction. Differences between groups and correlations among the study variables were analyzed.ResultsMusculoskeletal pain at any point in life was reported by 91.8% of participants, most commonly in the neck, lower back, and upper back. Musculoskeletal disorder scores were negatively correlated with job satisfaction and positively correlated with emotional exhaustion, depersonalization, and total burnout scores. Strong negative correlations were observed between job satisfaction and burnout subdimensions. Technicians in private hospitals had lower job satisfaction and higher emotional exhaustion and depersonalization scores than those in public hospitals.ConclusionsWMSDs are highly prevalent among physiotherapy technicians and are significantly associated with increased burnout and reduced job satisfaction. Implementing workplace ergonomic interventions, improving working conditions, and providing ergonomics training are essential to protect employee health and enhance work performance.