‘Do As We Say, Not as We Do:’ a cross-sectional survey of injuries in injury prevention professionals
Bibliographic record
Abstract
BACKGROUND: As the leading cause of death and among the top causes of hospitalisation in Canadians aged 1-44 years, injury is a major public health concern. Little is known about whether knowledge, training and understanding of the underlying causes and mechanisms of injury would help with one's own prevention efforts. Based on the Theory of Planned Behaviour, we hypothesised that injury prevention professionals would experience fewer injuries than the general population. METHODS: An online cross-sectional survey was distributed to Canadian injury prevention practitioners, researchers and policy makers to collect information on medically attended injuries. Relative risk of injury in the past 12 months was calculated by comparing the survey data with injury incidence reported by a comparable subgroup of adults from the (Canadian Community Health Survey (CCHS)) from 2009 to 2010. RESULTS: We had 408 injury prevention professionals complete the survey: 344 (84.5%) women and 63 (15.5%) men. In the previous 12 months, 86 individuals reported experiencing at least one medically attended injury (21,235 people per 100,000 people); with sports being the most common mechanism (41, 33.6%). Fully 84.8% individuals from our sample believed that working in the field had made them more careful. After accounting for age distribution, education level and employment status, injury prevention professionals were 1.69 (95% CI 1.41 to 2.03) times more likely to be injured in the past year. INTERPRETATION: Despite their convictions of increasing their own safety behaviour and that of others, injury prevention professionals' knowledge and training did not help them prevent their own injuries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".