Defeating abusive supervision: Training supervisors to support subordinates.
Bibliographic record
Abstract
Although much is known about the antecedents and consequences of abusive supervision, scant attention has been paid to investigating procedures to reduce its frequency. We conducted a quasiexperiment to examine the effects of supervisor support training on subordinate perceptions of abusive supervision and supervisor support. Supervisors (n = 23) in 4 restaurants were trained in 4 supportive supervision strategies (benevolence, sincerity, fairness, and experiential processing) during 4 2-hr sessions over a period of 2 months. We compared perceived supervisor support and abusive supervision before and 9 months after training for 208 employees whose supervisors received support training and 241 employees in 4 similar control restaurants. Compared to employees in the control restaurants, employees whose supervisors received the support training reported higher levels of perceived supervisor support and less abusive supervision. These findings suggest that a relatively brief training program can help managers become more supportive and less abusive. Theoretical and practical implications for effectively managing abusive supervision are discussed. (PsycINFO Database Record
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".