Impact of Energy-Based Safety Training on Quality of Prejob Safety Meetings and Control of Hazardous Energy in Construction: Multiple Baseline Experiment
Bibliographic record
Abstract
Serious injuries and fatalities (SIFs) continue to plague the construction industry. The preponderance of evidence suggests that preventing SIFs requires the identification, assessment, and control of hazardous energy. In this study, we isolated and measured the impact of energy-based safety training on the quality of prejob safety briefs and the presence of direct controls during subsequent work. We conducted a standardized training intervention in both English and Spanish and tested it via a multiple baseline experiment on 10 construction crews working in the US and Canada. Dependent variables were measured using a prejob safety brief quality scoring rubric and the High-Energy Control Assessment (HECA) protocol. The training caused immediate and significant improvements in the quality of prejob safety briefs and a measurable but smaller effect on the HECA score. Whereas prejob safety meeting scores and effect sizes were consistent, HECA was highly variable across the work crews both before and after the training. This suggests that, although short-term impacts on the quality of safety planning may occur over a short time frame, the impacts on the control of hazardous energy may require comparatively more time and data to achieve conclusive results. Methodologically, this study demonstrates an experimental protocol for isolating and attributing the impact of a safety intervention over short periods. Such a protocol may be used in practice to draw casual inferences, and is a step toward the ability to objectively measure the return on safety investment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".