The purpose of the this research work was to evaluate the impact of social support (through its two dimensions: co-worker support and supervisor support) on the reduction of the burnout syndrome (considering its three dimensions: emotional exhaustion, professional efficacy, and cynicism) from two proposed models that show clear differences. Social support was assessed using the Job Content Questionnaire instrument, and the burnout syndrome was measured through the Maslach Burnout Inventory - General Survey. A total of 467 middle and senior managers from the Mexican manufacturing industry participated in the research. Structural Equation Modeling was used to determine the relationships between the proposed variables. The results of model one show that social support helps mitigate the effects of burnout syndrome in its three dimensions. On the other hand, model two features better fit indices and specifically shows that co-worker support has a direct negative relationship with the dimension of emotional exhaustion, while supervisor support directly and negatively affects cynicism. These results show that, although social support helps in the mitigation of burnout syndrome in general, each type of support separately has a direct impact on a specific dimension of the burnout syndrome. Clarifying these relationships will help in the proposal of occupational hazard management policies in the Mexican industrial sector.

Due to globalization and the accelerated growth of technology, ever more employees of companies are affected by burnout syndrome, the psychological nature of which requires a prolonged response to chronic interpersonal stressors in work environments. The present research aims to validate the operability of the Maslach Burnout Inventory-General Survey (MBI-GS) using a sample of 378 professionals belonging to middle and senior management working in companies within the IMMEX sector (comprising the industrial-manufacturing, maquiladora and export services) located in the state of Baja California, Mexico. Firstly, an exploratory factor analysis using the principal components method and Varimax rotation was performed and the results revealed the existence of three factors representing more than 67 percent of the total variance. Secondly, a confirmatory factorial analysis was carried out performing appropriate results for the indices Chi-square goodness-of-fit model, Root Mean Square Error of Approximation (RMSEA), Normed Fit Index (NFI), Comparative Fit Index (CFI), Relative Fit Index (RFI), Parsimony Ratio (PRATIO) and Parsimony Normed Fit Index (PNFI), which are highly recommended by literature in these types of studies. Additionally, construct validity was satisfactorily verified. The factorial solution coincided with the Maslach Burnout Inventory original proposal so that this instrument can be considered a valid and reliable option to analyze the burnout levels in people pertaining to middle and senior management in these types of industries.

This article aims to provide valuable new insights into organizations implementing Lean Manufacturing (LM) as a continuous improvement strategy, focusing on those which are reducing waste in order to increase their competitiveness. A statistical approach was used to model the causal relationships between LM Critical Success Factors (CSFs) to achieve this goal. We used an instrument which has been previously validated in the transportation equipment manufacturing sector in the Mexican manufacturing industry. The proposed hypotheses were subjected to empirical tests using the Structural Equation Modeling (SEM) technique. The results indicate that Top Management Involvement and Commitment, with Project Leadership as a mediator, contributes indirectly and significantly to the increase in the benefits of LM projects. In addition, it was observed that Customer Focus, Linking Lean to the Suppliers, and Training and Education directly influence the increase in the benefits of LM projects in the sector. These findings offer a frame of reference for manufacturing industries with similar characteristics to the sector in this study who wish to increase their benefits by developing projects using LM methodology.

Work-related stress is one of the ten most frequent occupational health problems worldwide and precedes a variety of serious mental disorders. It occurs when employees’ capabilities fail to meet job demands or when their knowledge and skills are insufficient to comply with company expectations. The aim of this research was to evaluate the impact of working hours and burnout syndrome (BS) on the appearance of musculoskeletal discomfort (MSD) through three dimensions. A cross-sectional study was conducted on 472 middle and senior management staff in the industrial sector in Mexico. Structural Equation Modeling was used to evaluate the relationship between variables. The results showed that 54% of participants reported to be working a maximum of 48 hours a week, the four most frequent MSD occurring in the low back, upper back, neck and eyes. Regarding BS, 59.6% of the participants experienced it at levels ranging from medium to extreme. These findings showed that working hours have a direct impact on emotional exhaustion and indirectly affect MSD and the dimensions of professional efficacy and cynicism. In addition, discomfort in the upper back is directly associated to ailments in the low back and neck and indirectly, to discomfort in the eyes. This points to the need for a greater focus on developing specific measures to reduce or prevent musculoskeletal discomfort symptoms which result from employees’ long working hours and emotional exhaustion.

Background/Objectives: Efficient management of intensive care unit (ICU) resources is a critical challenge for modern healthcare systems, which must balance high-quality patient care with operational and financial performance. ICU length of stay (LOS) is a key metric of clinical complexity and hospital efficiency. However, traditional methods for predicting LOS often fail to capture the complex, nonlinear interactions among physiological, demographic, and treatment-related variables. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for enhancing predictive accuracy and supporting data-driven decision-making. Methods: This study presents a systematic review and meta-analysis of ML and DL approaches for predicting ICU LOS in adult patients. Following PRISMA guidelines, eight scientific databases were searched, yielding 33 eligible studies published between 2015 and 2025. Results: Mixed medical–surgical ICUs were the most common setting (51.5%), and 45.5% of datasets were sourced from public repositories. Most studies (19/33) focused on binary classification of prolonged stays, although thresholds ranged from >48 h to ≥14 days. The pooled results from ten studies yielded an AUROC of 0.9005 (95% CI: 0.8890–0.9121), indicating strong predictive capability across diverse clinical contexts. Subgroup analyses showed comparable performance between specialized surgical and general ICUs. Conclusions: These findings suggest that AI-driven LOS prediction models exhibit strong discriminatory power for ICU LOS prediction, supporting hospital capacity planning. However, to translate this into reliable clinical support, the methodological heterogeneity, scarcity of external validation, and near absence of calibration reporting identified in this review need to be addressed.