Transformational Leadership and Employee Safety Performance: A Within-Person, Between-Jobs Design
Bibliographic record
Abstract
We investigated the extent to which the safety performance (i.e., self-reported safety compliance and safety participation) of employees with 2 jobs was predicted by their respective supervisors' transformational leadership behaviors. We compared 2 within-person models: a context-specific model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance only in that context) and a context-spillover model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance in the same and other contexts). Our sample comprised 159 “moonlighters” (73 men, 86 women): employees who simultaneously hold 2 different jobs, each with a different supervisor, providing within-person data on the influence of different supervisors on employee safety performance across 2 job contexts. Having controlled for individual differences (negative affectivity and conscientiousness) and work characteristics (e.g., hours worked and length of relationship with supervisor), the context-specific model provided the best fit to the data among alternative nested models. Implications for the role of transformational leadership in promoting workplace safety 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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".