搜索结果：Burnout

Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and r

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.

Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine learning (ML) approaches, and focused on detecting burnout in software developers and IT professionals. Our objective is to review the accuracy and precision of the proposed ML techniques, and to formulate recommendations for future researchers interested to replicate or extend those studies. From our SLR we observed that a majority of primary studies focuses on detecting emotions or utilise emotional dimensions to detect or predict the presence of burnout. We also performed a cross-sectional study to detect which ML approach shows a better performance at detecting emotions; and which dataset has more potential and expressivity to capture emotions. We believe that, by identifying which ML tools and datasets show a better performance at detecting emotions, and indirectly at identifying burnout, our paper can be a valuable asset

Burnout affects software developers' mental and physical well-being and contributes to turnover, generating strong concerns in the software industry. Prior research has shown that lack of belonging is associated with higher levels of burnout among software developers, while a sense of belonging is linked to resilience, job satisfaction, engagement, and well-being. In this paper, we revisit recent studies on belongingness in software development teams, including proprietary software organizations and open-source software communities, to offer evidence-based guidelines for cultivating belongingness and reducing developer burnout. We summarize characteristics of belongingness, such as trust, acceptance, value recognition, friendship, membership, mutual support, and being known by others, as well as factors associated with belongingness, including recognition, psychological safety, intrinsic motivation, English confidence, tenure, gender, and cultural power distance. Based on these findings, we propose practical guidelines for leaders and communities, including timely and consistent recognition, transparent promotion rules, inclusive benefits and initiatives, intentional connections th

Changes are inherent in software development, often increasing developers' perception of instability. Understanding the relationship between human factors and Software Engineering processes is crucial to mitigating and preventing issues. One such factor is burnout, a recognized disease that impacts productivity, turnover, and, most importantly, developers' well-being. Investigating the link between instability and burnout can help organizations implement strategies to improve developers' work conditions and performance. This study aims to identify and describe the relationship between perceived instability and burnout among software developers. A cross-sectional survey was conducted with 411 respondents, using convenience sampling and self-selection. In addition to analyzing variable relationships, confirmatory factor analysis was applied. Key findings include: (1) A significant positive relationship between burnout (exhaustion and cynicism) and team, technological, and task instability; (2) A weak negative relationship between efficacy and technological/team instability, with no correlation to task instability; (3) Exhaustion was the most frequently reported burnout symptom, while

This paper addresses the critical issue of burnout among cybersecurity professionals, a growing concern that threatens the effectiveness of digital defense systems. As the industry faces a significant attrition crisis, with nearly 46% of cybersecurity leaders contemplating departure from their roles, it is imperative to explore the causes and consequences of burnout through a socio-technical lens. These challenges were discussed by experts from academia and industry in a multi-disciplinary workshop at the 26th International Conference on Human-Computer Interaction to address broad antecedents of burnout, manifestation and its consequences among cybersecurity professionals, as well as programs to mitigate impacts from burnout. Central to the analysis is an empirical study of former National Security Agency (NSA) tactical cyber operators. This paper presents key insights in the following areas based on discussions in the workshop: lessons for public and private sectors from the NSA study, a comparative review of addressing burnout in the healthcare profession. It also outlines a roadmap for future collaborative research, thereby informing interdisciplinary studies.

Burnout, classified as a syndrome in the ICD-11, arises from chronic workplace stress that has not been effectively managed. It is characterized by exhaustion, cynicism, and reduced professional efficacy, and estimates of its prevalence vary significantly due to inconsistent measurement methods. Recent advancements in Natural Language Processing (NLP) and machine learning offer promising tools for detecting burnout through textual data analysis, with studies demonstrating high predictive accuracy. This paper contributes to burnout detection in German texts by: (a) collecting an anonymous real-world dataset including free-text answers and Oldenburg Burnout Inventory (OLBI) responses; (b) demonstrating the limitations of a GermanBERT-based classifier trained on online data; (c) presenting two versions of a curated BurnoutExpressions dataset, which yielded models that perform well in real-world applications; and (d) providing qualitative insights from an interdisciplinary focus group on the interpretability of AI models used for burnout detection. Our findings emphasize the need for greater collaboration between AI researchers and clinical experts to refine burnout detection models. A

Using a systematic review and meta-analysis, this study investigates the impact of the COVID-19 pandemic on job burnout among nurses. We review healthcare articles following the PRISMA 2020 guidelines and identify the main aspects and factors of burnout among nurses during the pandemic. Using the Maslach Burnout questionnaire, we searched PubMed, ScienceDirect, and Google Scholar, three open-access databases, for relevant sources measuring emotional burnout, personal failure, and nurse depersonalization. Two reviewers extract and screen data from the sources and evaluate the risk of bias. The analysis reveals that 2.75% of nurses experienced job burnout during the pandemic, with a 95% confidence interval and rates varying from 1.87% to 7.75%. These findings emphasize the need for interventions to address the pandemic's effect on job burnout among nurses and enhance their well-being and healthcare quality. We recommend considering individual, organizational, and contextual factors influencing healthcare workers' burnout. Future research should focus on identifying effective interventions to lower burnout in nurses and other healthcare professionals during pandemics and high-stress s

Orientation: The study explores the connections among servant leadership, burnout, and work ethic culture in organizations. It aims to provide a detailed understanding of how servant leadership influences work ethic culture, especially by considering the role of burnout. Research Purpose: This study aims to understand how servant leadership influences work ethic culture and explore the mediating role of burnout in this relationship. Motivation for the Study: This study wants to fill gaps in our understanding of how servant leadership, burnout, and work ethic culture are connected. It seeks to add useful insights to what we already know from previous research. Research Approach/Design and Method: The study, using surveys and statistics, examines the links between servant leadership, burnout, and work ethic culture in 113 hotels in Bandung, Indonesia, with 339 participants. A 183-sample, chosen with a 0.05 margin of error, underwent SEM-PLS analysis using SmartPLS 3.0. Main Findings: The key findings underscore that servant leadership exerts a positive influence on work ethic culture, and burnout plays a pivotal mediating role in this dynamic. The results shed light on the intricate

We study the aggregate hazard rate of a heterogeneous population whose individual event intensities are modeled as Cox (doubly stochastic) processes. In the deterministic hazard setting, the observed pool hazard is the survival weighted mean of the individual hazards, and its time derivative equals the mean individual hazard drift minus a variance term. This yields a transparent structural explanation of burnout in mortgage pools. We extend this perspective to stochastic intensity models. The observed pool hazard remains a survival-weighted mean, but now evolves as an Ito process whose drift contains the mean drift of the individual hazards and a negative selection term driven by cross-sectional dispersion, together with a diffusion term inherited from the common factor. We formulate the general identity and discuss special cases relevant to mortgage prepayment modeling.

Generative AI (GenAI) is rapidly reshaping software development workflows. While prior studies emphasize productivity gains, the adoption of GenAI also introduces new pressures that may harm developers' well-being. In this paper, we investigate the relationship between the adoption of GenAI and developers' burnout. We utilized the Job Demands--Resources (JD--R) model as the analytic lens in our empirical study. We employed a concurrent embedded mixed-methods research design, integrating quantitative and qualitative evidence. We first surveyed 442 developers across diverse organizations, roles, and levels of experience. We then employed Partial Least Squares--Structural Equation Modeling (PLS-SEM) and regression to model the relationships among job demands, job resources, and burnout, complemented by a qualitative analysis of open-ended responses to contextualize the quantitative findings. Our results show that GenAI adoption heightens burnout by increasing job demands, while job resources and positive perceptions of GenAI mitigate these effects, reframing adoption as an opportunity.

The sustainability of Security Operations Centers depends on their people, yet 71% of practitioners report burnout and 24% plan to exit cybersecurity entirely. Flow theory suggests that when job demands misalign with practitioner capabilities, work becomes overwhelming or tedious rather than engaging. Achieving challenge-skill balance begins at hiring: if job descriptions inaccurately portray requirements, organizations risk recruiting underskilled practitioners who face anxiety or overskilled ones who experience boredom. Yet we lack empirical understanding of what current SOC job descriptions actually specify. We analyzed 106 public SOC job postings from November to December 2024 across 35 organizations in 11 countries, covering Analysts (n=17), Incident Responders (n=38), Threat Hunters (n=39), and SOC Managers (n=12). Using Inductive Content Analysis, we coded certifications, technical skills, soft skills, tasks, and experience requirements. Three patterns emerged: (1) Communication skills dominate (50.9% of postings), exceeding SIEM tools (18.9%) or programming (30.2%), suggesting organizations prioritize collaboration over technical capabilities. (2) Certification expectations

We consider large-scale systems influenced by burnout variables - state variables that start active, shape dynamics, and irreversibly deactivate once certain conditions are met. Simulating what-if scenarios in such systems is computationally demanding, as alternative trajectories often require sequential processing, which does not scale very well. This challenge arises in settings like online advertising, because of campaigns budgets, complicating counterfactual analysis despite rich data availability. We introduce a new type of algorithms based on what we refer to as uncertainty relaxation, that enables efficient parallel computation, significantly improving scalability for counterfactual estimation in systems with burnout variables.

Clinician burnout poses a substantial threat to patient safety, particularly in high-acuity intensive care units (ICUs). Existing research predominantly relies on retrospective survey tools or broad electronic health record (EHR) metadata, often overlooking the valuable narrative information embedded in clinical notes. In this study, we analyze 10,000 ICU discharge summaries from MIMIC-IV, a publicly available database derived from the electronic health records of Beth Israel Deaconess Medical Center. The dataset encompasses diverse patient data, including vital signs, medical orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. We introduce a hybrid pipeline that combines BioBERT sentiment embeddings fine-tuned for clinical narratives, a lexical stress lexicon tailored for clinician burnout surveillance, and five-topic latent Dirichlet allocation (LDA) with workload proxies. A provider-level logistic regression classifier achieves a precision of 0.80, a recall of 0.89, and an F1 score of 0.84 on a stratified hold-out set, surpassing metadata-only baselines by greater than or equal to 0.17 F1 score. Specialty-specific analysis indicates elevated bur

In recent years, and particularly during the Covid-19 pandemic, Morocco has experienced significant pressure from user demand, leading to a significant workload in public hospitals. This situation raises major questions regarding the occupational health of healthcare staff. While previous studies have focused on the role of AI in the safety and resilience of military personnel, no research has investigated its role in protecting healthcare personnel from psychosocial risks. This inadequacy leads us to formulate the following central question:What is the contribution of machine learning to the prevention of emotional exhaustion (burnout) among healthcare staff in Morocco? This work is part of a modeling approach aimed at developing a predictive model of the risks of emotional exhaustion (burn-out), the parameters of which will be estimated using supervised learning. From a scientific perspective, this work aims to contribute to the development of systems for preventing psychosocial risks affecting staff in healthcare establishments. From a managerial perspective, this research aims to equip decision-makers in healthcare establishments so that they can anticipate psychosocial disorde

In this study, the relationship between burnout and family functions of the Melli Iran Bank staff will be studied. A number of employees within the organization using appropriate scientific methods as the samples were selected by detailed questionnaire and the appropriate data is collected burnout and family functions. The method used descriptive statistical population used for this study consisted of 314 bank loan officers in branches of Melli Iran Bank of Tehran province and all the officials at the bank for >5 years of service at Melli Iran Bank branches in Tehran. They are married and men constitute the study population. The Maslach Burnout Inventory in the end internal to 0/90 alpha emotional exhaustion, depersonalization and low personal accomplishment Cronbach alpha of 0/79 and inventory by 0/71 within the last family to solve the problem 0/70, emotional response 0/51, touch 0/70, 0/69 affective involvement, roles, 0/59, 0/68 behavior is controlled. The results indicate that the hypothesis that included the relationship between burnout and 6, the family functioning, problem solving, communication, roles, affective responsiveness, affective fusion there was a significant r

Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting physician burnout based on electronic health record (EHR) activity logs, digital traces of physician work activities that are available in any EHR system. In contrast to prior approaches that exclusively relied on surveys for burnout measurement, our framework directly learns deep representations of physician behaviors from large-scale clinician activity logs to predict burnout. We propose the Hierarchical burnout Prediction based on Activity Logs (HiPAL), featuring a pre-trained time-dependent activity embedding mechanism tailored for activity logs and a hierarchical predictive model, which mirrors the natural hierarchical structure of clinician activity logs and captures physicians' evolving burnout risk at both short-term and long-term levels. To utilize the large amount of unlabeled activity logs, we propose a semi-supervised framework that learns to transfer knowledge extracted from unlabeled clinician activities to the HiPAL-based prediction model. The experiment on over 15 million clinician activity

Medical residency training is often associated with physically intense and emotionally demanding tasks, requiring them to engage in extended working hours providing complex clinical care. Residents are hence susceptible to negative psychological effects, including stress and anxiety, that can lead to decreased well-being, affecting them achieving desired training outcomes. Understanding the daily behavioral patterns of residents can guide the researchers to identify the source of stress in residency training, offering unique opportunities to improve residency programs. In this study, we investigate the workplace behavioral patterns of 43 medical residents across different stages of their training, using longitudinal wearable recordings collected over a 3-week rotation. Specifically, we explore their ambulatory patterns, the computer access, and the interactions with mentors of residents. Our analysis reveals that residents showed distinct working behaviors in walking movement patterns and computer usage compared to different years in the program. Moreover, we identify that interaction patterns with mentoring doctors indicate stress, burnout, and job satisfaction.