BACKGROUND: A quantitative comfort model will aid in evaluating comfort levels of various target groups before the actual flight of an airplane. However, constructing the model is always a challenge due to the complexity of the phenomenon. OBJECTIVES: In this paper, we present quantitative comfort models to predict the (dis)comfort of passengers flying with turboprops based on objective measures. METHODS: Ninety-seven participants took part in two experiments conducted during real flights, during which forty of them had environmental and personal factors recorded using (self-developed) measurement tools. The collected data were analyzed to model the relations between objective measures and subjective feelings. RESULTS: Two preliminary models based on gradient boosting regression were developed. The models were able to predict the changes in comfort and discomfort of individual passengers with an accuracy of 0.12±0.01 and 0.21±0.01 regarding normalized comfort and discomfort scores, respectively. Additionally, contributions of different factors were highlighted. CONCLUSION: The outcomes of the models show that we took a step forward in modeling the human comfort experience using objective measurements. Anthropometry (including age), seat positions, time duration, and row (noise) emerged as leading factors influencing the feeling of (dis)comfort in turboprop planes.
BackgroundJob satisfaction is a key determinant of employee well-being and organizational performance. However, most existing measures assess it as a static construct, overlooking its dynamic and temporal nature.ObjectivesTo address this gap, the aim of this study was to develop the Temporal Job Satisfaction Scale (TJSS), a new instrument designed to evaluate job satisfaction across past, present, and future dimensions.MethodsThe TJSS was developed by adapting positively balanced items from the Job Satisfaction Scale to three temporal frames, yielding a 9-item measure. The scale was administered to 245 Italian workers, alongside measures of life satisfaction, self-efficacy, and self-reported job performance.ResultsConfirmatory factor analyses (CFA) supported a bifactor model comprising one general factor and three orthogonal temporal dimensions. Internal consistency was high for all subscales and for the overall score. TJSS scores correlated positively with life satisfaction, self-efficacy, and performance. Structural equation modeling (SEM) results supported an indirect-effects model, with job satisfaction mediating the links between self-efficacy and both job performance and life satisfaction.ConclusionsThe TJSS provides initial evidence of reliability and validity and represents a theoretically grounded tool for capturing the temporal complexity of job satisfaction. Its dynamic structure enables a more nuanced understanding of employee experiences over time and highlights the motivational role of self-efficacy in shaping satisfaction and performance. The scale holds promise for both organizational research and practical interventions.
Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern worldwide, significantly affecting employees’ well-being and productivity. Government employees in Bangladesh are particularly vulnerable to WMSDs due to the sedentary or repetitive nature of their work (e.g., prolonged computer use, repetitive typing, and manual mechanical tasks). Objective This study aimed to determine the clinical distribution, patterns, and associated factors of WMSDs among government employees in Bangladesh and evaluate the subjective impact on workers’ productivity and quality of life. Methods A retrospective descriptive cross-sectional study was conducted at the Government Employees Hospital in Dhaka, Bangladesh. Participants were recruited using consecutive sampling from government employees attending the Physical Medicine & Rehabilitation Department. Descriptive and inferential statistics (Chi-square and Fisher's exact tests via R software) were used to examine the distribution of conditions like low back pain, knee osteoarthritis, cervical spondylosis, frozen shoulder. Productivity and quality of life were assessed through structured clinical interviews, while demographic characteristics, work type, and employment grade were analyzed to identify associated factors. Results Nonspecific low back pain was the most prevalent condition (27.23%), particularly among retired employees (34.62%) and sedentary workers (26.97%). Knee osteoarthritis (10.81%) and cervical spondylosis (8.97%) were more common among older and dynamic workers. The most commonly affected sites were the lower back (43.73%), knees, shoulders, and neck. Disease patterns varied significantly by age, and government service grade. Conclusion The high proportion of diagnosed cases among Bangladeshi government employees underscores the need for interventions including ergonomic modifications, regular health assessments, and early screening strategies to reduce WMSD impact and inform public sector health policies.
Background The global workforce is aging, with workers aged 55 and over projected to constitute approximately one quarter of the labor force in most OECD countries by 2030. Governments have responded with legislative and policy frameworks to support aging worker participation, but whether these frameworks provide employers with adequate guidance to manage age-related occupational risks remains unclear. Objective To compare regulatory approaches to aging workforce safety across ten countries and identify common gaps. Methods In this comparative policy analysis, ten countries were examined: Australia, the United States, the United Kingdom, Japan, China, Argentina, South Africa, the Czech Republic, France, and Singapore. Legislation, government guidance, national action plans, and occupational health and safety provisions for aging workers were analyzed. Results All ten countries have enacted measures addressing aging workforce participation. A consistent pattern emerges: regulatory frameworks place primary responsibility on employers to identify and manage age-related risks without providing specific guidance on what those risks are or how to assess them. Frameworks are predominantly reactive. Conclusions The global regulatory landscape is characterized by employer obligation without adequate support. Targeted risk assessment tools and pre-emptive regulatory guidance are needed to reduce occupational injury and illness among older workers.
Background Artificial intelligence (AI) is increasingly used in public administration, creating new challenges involving accountability, transparency, explainability, and workforce preparedness. Although state governments are developing AI governance frameworks, it remains unclear whether accountability principles are translated into practical mechanisms for governing AI use. Objective This study examines how state AI governance policies operationalize accountability in public administration and identifies remaining gaps in responsibility, oversight, and enforceability. Methods A qualitative comparative policy analysis was conducted using publicly available AI governance frameworks from 24 U.S. states identified through March 2026. The documents were analyzed using thematic categories addressing accountability and related governance mechanisms, including transparency, human oversight, auditing, risk management, training, documentation, public notice, appeals, and enforcement. The study distinguished between symbolic accountability, in which accountability is expressed primarily as a governance principle, and operational accountability, in which policies establish identifiable mechanisms for implementation and oversight. Results Eighteen of the 24 states explicitly identified accountability as an AI governance principle. However, accountability was frequently expressed symbolically rather than through operational mechanisms. State frameworks more consistently addressed principles such as transparency and risk management while providing less direction regarding enforcement, appeals, independent review, documentation, monitoring, and responsibility for harmful AI-assisted outcomes. Conclusions State governments increasingly recognize accountability as an important component of AI governance, but mechanisms for putting accountability into practice remain uneven. Effective public-sector AI governance requires clearer responsibility structures, human oversight, documentation, auditability, review and appeal procedures, and workforce preparation. Without these mechanisms, accountability may remain largely symbolic and responsibility may be shifted toward public-sector workers who are expected to use and justify AI-assisted decisions.
