Metrics to assess injury prevention programs for young workers in high-risk occupations: a scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: Despite legal protections for young workers in Canada, youth aged 15-24 are at high risk of traumatic occupational injury. While many injury prevention initiatives targeting young workers exist, the challenge faced by youth advocates and employers is deciding what aspect(s) of prevention will be the most effective focus for their efforts. A review of the academic and grey literatures was undertaken to compile the metrics-both the indicators being evaluated and the methods of measurement-commonly used to assess injury prevention programs for young workers. Metrics are standards of measurement through which efficiency, performance, progress, or quality of a plan, process, or product can be assessed. METHODS: A PICO framework was used to develop search terms. Medline, PubMed, OVID, EMBASE, CCOHS, PsychINFO, CINAHL, NIOSHTIC, Google Scholar and the grey literature were searched for articles in English, published between 1975-2015. Two independent reviewers screened the resulting list and categorized the metrics in three domains of injury prevention: Education, Environment and Enforcement. RESULTS: Of 174 acquired articles meeting the inclusion criteria, 21 both described and assessed an intervention. Half were educational in nature (N=11). Commonly assessed metrics included: knowledge, perceptions, self-reported behaviours or intentions, hazardous exposures, injury claims, and injury counts. One study outlined a method for developing metrics to predict injury rates. CONCLUSION: Metrics specific to the evaluation of young worker injury prevention programs are needed, as current metrics are insufficient to predict reduced injuries following program implementation. One study, which the review brought to light, could be an appropriate model for future research to develop valid leading metrics specific to young workers, and then apply these metrics to injury prevention programs for youth.
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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 | 0.039 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.058 | 0.049 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".