A Moderated Mediation Analysis of New Work-Related Stressors, Psychological Distress, and Absenteeism in Health Care During a Pandemic
Bibliographic record
Abstract
OBJECTIVES: We aimed to evaluate the effects of new work-related stressors on psychological distress and absenteeism and the role of recognition in these relationships. METHODS: Moderated path analyses were carried out on a sample of 1128 health care workers. RESULTS: Increased workload related to COVID-19 (coronavirus disease 2019) ( β = 1.511, P ≤ 0.01) and fear of COVID-19 ( β = 0.844, P ≤ 0.01) were directly associated with a higher level of psychological distress and indirectly ( β = 2.306, P ≤ 0.01; and β = 1.289, P ≤ 0.05, respectively) associated with a higher level of absenteeism. Recognition ( β = 0.260, P ≤ 0.001) moderated the association between teleworking and psychological distress. Furthermore, this significant moderation effect had a significant impact on absenteeism ( β = 0.392, P ≤ 0.05). Regardless of the workplace (on site or teleworking), high recognition was beneficial for psychological distress. This effect seems more important when working on site. CONCLUSIONS: The results propose that specific new work-related stressors should be addressed in the context of organizational change (eg, a pandemic).
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".