Leading the way to safety : an investigation of S.A.F.E.R. Leadership
Bibliographic record
Abstract
Work related injuries and fatalities can cause significant human suffering as well as considerable social and economic costs.A growing body of research has demonstrated that leaders can play an important role in enhancing safety at work.However, most studies have relied on existing models of leadership, such as transformational leadership, to investigate the impact of leadership on safety outcomes.Furthermore, most studies have used cross-sectional research designs, which is a gap in the literature considering how the relationships between leaders and followers occur over time.This dissertation aimed to address these gaps over the course of three studies.In study 1, a new scale of safety leadership was developed based on the S.A.F.E.R Leadership Model (Wong, Kelloway, & Makhan, 2015).The S.A.F.E.R Leadership Scale demonstrated good convergent and concurrent validity, as well as incremental validity above and beyond two existing measures of safety leadership.Study 2 adopted a cross-lagged research design to investigate the temporal relationships between safety leadership, safety climate, and safety performance (i.e., safety compliance and safety participation) using a sample of transit workers.The findings suggest that S.A.F.E.R leadership predicts safety climate and performance over time, demonstrating predictive validity, and the direction of causality is from S.A.F.E.R leadership to the outcomes, and not vice versa.Study 3 also adopted a temporal design, examining impact of workload on S.A.F.E.R leadership in a training context.An analysis of the post-training growth trajectories of workload and S.A.F.E.R leadership suggested that workload was not a barrier to transfer of training for nurse leaders.Taken together, this dissertation demonstrates that the S.A.F.E.R Leadership Model is a viable model of safety leadership that is different from the existing conceptualizations of safety leadership, and provides a psychometrically sound measure of S.A.F.E.R Leadership that can be used in training to enhance safety behaviours and outcomes in organizations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".