Understanding the organizational performance metric, an occupational health and safety management tool, through workplace case studies
Bibliographic record
Abstract
Purpose The purpose of this paper is to use a comparative qualitative case study design to better understand how the observed characteristics of an organization correspond to their score on the organizational performance metric (IWH-OPM), a leading indicator tool designed to measure an organization’s occupational health and safety (OHS) performance. Design/methodology/approach Five organizations were recruited based on their diverse IWH-OPM scores obtained in a previous study. Qualitative data were collected from these cases and analyzed with consideration of OHS leadership; OHS culture and climate; employee participation in OHS; OHS policies, procedures and practices; and OHS risk control. Similarities and differences among organizations were examined in relation to these themes. Findings Three distinct groups of firms emerged from the cross-case analysis in terms of their overall OHS performance: high, medium and low. Higher firm IWH-OPM scores generally corresponded to better OHS performance in the workplace as observed through qualitative methods. Originality/value The findings are a step toward OHS leaders or practitioners eventually being able, based on an organization’s IWH-OPM score, to have a quick understanding of a workplace’s OHS status and of how best to support further improvement.
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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".