Mechanisms Explaining the Longitudinal Effect of Psychosocial Safety Climate on Work Engagement and Emotional Exhaustion among Education and Healthcare Professionals during the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, the education and healthcare sectors were severely affected. There is a need to investigate the ways in which these workers in at-risk sectors can be protected and through what mechanisms. The aims of this research are, therefore, (1) to assess the mediating role of job demands and resources in the relationship between psychosocial safety climate (PSC) and work engagement and emotional exhaustion, and (2) to test for sector-specific differences among education and healthcare professionals during the COVID-19 pandemic. In the study, which employed a longitudinal design including three measurement times, 70 education professionals and 69 healthcare professionals completed a questionnaire measuring PSC, psychological demands, social support, recognition, work engagement, and emotional exhaustion. The results show that PSC was significantly higher among education professionals than among healthcare professionals. When considering both job sectors together, mediation analyses show that social support mediates the PSC-work engagement relationship, while psychological demands mediate the PSC-emotional exhaustion relationship. Moderated mediation analyses show that job sector is a moderator: among education professionals, colleague support and recognition mediate the PSC-work engagement relationship, and psychological demands mediate the PSC-emotional exhaustion relationship. PSC is associated with more balanced job demands and resources, higher work engagement, and lower emotional exhaustion among education and healthcare professionals. The study of these two sectors, which are both vital to society but also more exposed to adverse work conditions, shows the importance that managers and executives must attach to their mental health by improving their respective working conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".