Why is your boss making you sick? A longitudinal investigation modeling time‐lagged relations between abusive supervision and employee physical health
Bibliographic record
Abstract
Summary Although an abundance of cross‐sectional data have linked abusive supervision with employees' experience of health‐related problems, further research accounting for the temporal dynamics of these variables is needed to establish causality. Furthermore, the process by which abusive supervision relates to subordinate health problems requires greater clarification. In a 1‐year longitudinal cross‐lagged investigation, we sought to test the time‐lagged relationship between abusive supervision and employee physical health; additionally, we test rumination as a cognitive process that mediates this time‐lagged relationship while modeling other relevant social and motivational mediators. Our results indicate that subordinate ruminative thinking about their experiences of abusive supervision mediates the time‐lagged association between abusive supervision and physical health problems. These findings suggest that reducing ruminative thinking may limit the long‐term impact of abusive supervision on employees' physical health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".