Organizational interventions and occupational burnout: a meta-analysis with focus on exhaustion

Cultivating a psychological health and safety culture for interprofessional primary care teams through a co-created evidence-informed toolkit

The psychological health and safety of healthcare workers workplaces and learning environments impacts the quality of healthcare services. To facilitate the psychological health and safety of interprofessional primary care teams, we curated a bilingual toolkit of 122 psychological health and safety resources comprising a multi-level categorization addressing individual, team, organization, and system-level interventions. The resources in the toolkit are organized by 7 themes, based on a clustering of the 15 psychosocial factors. Adopting the framework built on the 7 themes, this article describes the toolkit development process and how it addresses the key factors for psychologically healthy and safe workplaces to foster interprofessional collaboration. Implementation of the interventions in the toolkit is an important next step for which health system leadership is critical. Additionally, we identify several gaps and call on researchers, educators, and health leaders to address them in their future work.

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis

BACKGROUND

Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers.

METHODS

The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 10-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample.

RESULTS

Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = 0.811; balanced accuracy = 0.745, sensitivity = 0.765; specificity = 0.726). The best predictors of burnout were Beck's Depression Inventory scores, Athen's Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status.

CONCLUSIONS

The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Psychometric validity of the Shirom-Melamed Burnout Measure and the Burnout Assessment Tool: a systematic review

In the absence of internationally recognised standardised criteria, several patient-reported outcome measures (PROMs) have been developed to measure occupational burnout. The aim of this study was to extend our 2021 review of the psychometric validity of five PROMs to the Shirom-Melamed Burnout Measure (SMBM) and the Burnout Assessment Tool (BAT). To do that we ran a systematic literature search in the MEDLINE, PsycINFO, and Embase databases following our previous methodological framework and the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN). We assessed the level of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guideline. We identified 694 publications on SMBM and 421 on BAT, but the final review includes eight papers on SMBM and three on BAT. Of the seven psychometric properties assessed for SMBM, content, structural, and criterion validity were rated as insufficient, whereas the quality of evidence for construct and internal consistency was high and moderate, respectively. Of the nine psychometric properties assessed for BAT, content, structural, criterion, and construct validity was moderate and internal consistency was high. One limitation of this study is that we did not assess cross-cultural validity, because the number of studies reviewed is too small and content validity can only be assessed based on the original PROM version rather than translation. To conclude, BAT is superior to SMBM in terms of psychometric validity, but the quality of evidence for some properties is low or very low, suggesting a need for additional validation studies.