Psychological distress and post-traumatic growth in France during the COVID-19 pandemic: A mediation model of psychosocial safety climate as a determinant of work performance
Bibliographic record
Abstract
The psychosocial safety climate (PSC) reflects workers' perceptions of senior management's concern for mental health. Because the COVID-19 pandemic has exacerbated organizational issues, PSC could be a target for interventions attempting to preserve both the psychological health of employees and the economic health of companies. This study examines the direct and indirect relationships between PSC and work performance through two indicators of psychological health, psychological distress and post-traumatic growth, during a health crisis, i.e., prior to the second confinement in France. To this end, 2,004 participants from the French workforce completed a survey in October 2020. The results of mediation analyses indicate that PSC has a direct and positive influence on post-traumatic growth (PTG) and performance, as well as a direct negative influence on psychological distress. PSC also has an indirect positive influence on performance via psychological distress. Organizations that wish to jointly address mental health and performance at work would benefit from optimizing PSC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".