Why Improving the Safety Climate Doesn't Always Improve the Safety Performance
Bibliographic record
Abstract
Abstract This paper will discuss the results of a study outside the petrochemical industry (Vandevis (2008), but the results may have a profound effect on the way organizations try to influence their safety climate by setting so called SMART goals. It was conducted within the electrical high voltage contracting industry in Ontario, Canada and the objective was to investigate the relation between goal setting as a way to influence the safety climate and several safety related parameters and injury experiences. A quantitative survey based on Zohar's (2000) Safety Climate Scales was conducted with 564 surveys returned from 26 companies. Safety climate was not found to be correlated to self-reports of injury nor to lost-time or no-lost-time injury statistics. Safety climate was strongly and positively correlated with organizations setting at least partly SMART safety goals and in particular very strongly correlated when setting a goal of zero injuries and moderately and inversely correlated to self-reports of injury. People feel safer and report less accidents with or without injuries, but the actual performance using objective statistics has not improved. Setting such SMART safety goals as a way to improve the safety climate may lure both the management and employees of an organization into a false sense of achievement: the perceived level of safety goes up, however this change in perception is not matched with an actual improvement in safety performance. This study shows that management needs to do more than just setting SMART targets; if changes in the climate are seen as a "goal" rather than a "means" the safety performance will not improve.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".