In Search of Concrete Outcomes—A Systematic Review on the Effectiveness of Educational Interventions on Reducing Acute Occupational Injuries
Bibliographic record
Abstract
Education is a common strategy used to prevent occupational injuries. However, its effectiveness is often measured using surrogate measures instead of true injury outcomes. To evaluate the effectiveness of workplace educational interventions, we selectively analyzed studies that reported injury outcomes (PROSPERO ID: CRD42019140631). We searched databases for peer-reviewed journal articles and sources of grey literature such as abstracts, registered trials, and theses published between 2000 and 2019. Studies on educational interventions that reported fatal or non-fatal occupational injury outcomes were selected. Two reviewers independently and in duplicate screened the studies, extracted data, and assessed risk of bias. Heterogeneity in the data precluded meta-analysis, and the results were reviewed narratively. In total, 35 studies were included. Of which, 17 found a significant reduction in injuries, most of which featured a multifaceted approach or non-didactic education. The remaining studies either described equivocal results or did not report statistical significance. Overall, interventions in the manufacturing industry were more effective than those in the construction sector. Risk of bias among included studies was moderate to high. In conclusion, educational interventions could be an effective part of multifaceted injury prevention programs. However, over-reliance on didactic education alone is not advised.
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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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".