Differences in the Effectiveness of Three OHS Training Delivery Methods
Bibliographic record
Abstract
BACKGROUND: Methods of delivering occupational safety and health (OSH) training have shifted from in-person to online. Widespread delivery of a standardized OSH training course in three modalities in the province of Ontario, Canada allowed measurement of differences in their effectiveness. METHODS: Learners (N = 899) self-selected into face-to-face (F2F) instructor-led learning, online instructor-led synchronous distance learning, or online self-paced e-learning. Pre- and post-training surveys collected information on knowledge and other measures. Multiple regression analyses compared modalities on knowledge achievement (0%-100% scale; the primary outcome), engagement, perceived utility, perceived applicability, self-efficacy, and intention-to-use. RESULTS: F2F learners achieved a statistically significant 2.5% (95% CI: 0.3%, 4.7%) higher post-training knowledge score than distance learners (Cohen's d = 0.23, which is considered small). A statistically insignificant difference of 0.4% (95%: -1.4%, 2.3%) was seen between e-learners and distance learners. Collaborating training providers regarded these differences as not meaningful in practice. Statistically significant differences between modalities were seen for engagement, perceived utility, and self-efficacy. Scores of F2F learners were more favorable than scores of distance learners, which were, in turn, more favorable than scores of e-learners. CONCLUSIONS: This study provides evidence that there are small to no differences among F2F, distance and e-learning in their ability to ensure knowledge achievement among learners. This finding is likely generalizable to other well-designed short-term OSH training aimed at acquiring new knowledge. More research is needed to understand whether there are important differences across these modalities in basic OHS skill acquisition and transfer of learning to the workplace.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".