Safety in the c-suite: How chief executive officers influence organizational safety climate and employee injuries.
Bibliographic record
Abstract
According to social learning theory, powerful and high status individuals can significantly influence the behaviors of others. In this paper, we propose that chief executive officers (CEOs) indirectly impact frontline injuries through the collective social learning experiences and effort of different groups of organizational actors-including members of the top management team (TMT), organizational supervisors, and frontline employees. We found support for our collective social learning model using data from 2,714 frontline employees, 1,398 supervisors, and 229 members of TMTs in 54 organizations. TMT members' experiences within a CEO-driven TMT safety climate was positively related to organizational supervisors' reports of the broader organizational safety climate and their subsequent collective support for safety (reported by frontline employees). In turn, supervisory support for safety was associated with fewer employee injuries at the individual level. We discuss the theoretical and practical implications of these findings for workplace safety research and practice. (PsycINFO Database Record
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".