Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between abusive supervision and employee health and safety outcomes in Study 1 and to examine the effect of inconsistent leadership, operationalized as the interaction between transformational leadership and supervisor incivility, on employee safety participation in Study 2. Design/methodology/approach In Study 1, survey data were gathered fromn=145 healthcare workers. In Study 2, survey data were gathered fromn=177 nurses. Findings A partially mediated structural model was estimated in Study 1 and the results show that the model provided a good fit to the dataχ2(1)=1.27,p=0.23. Abusive supervision predicted safety climate (β=−0.41,p<0.01) and psychological health (β=−0.27,p<0.01). Safety climate, in turn, predicted psychological health (β= 0.40,p<0.01) and safety participation (β= 0.37,p<0.01). Study 2: moderated regression analysis showed that inconsistent leadership significantly predicted employee safety participation,F(5,144)=4.46,p<0.01. Originality/value Theoretical and practical implications for creating psychologically healthy workplaces through interventions aimed at improving leader effectiveness are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".