Factors related to self‐reported violent and accidental injuries
Bibliographic record
Abstract
Abstract The main objective of this study is to gain a better understanding of factors that distinguish violent and accidental injuries. A secondary analysis was conducted on data from a randomized telephone survey of 10 385 Canadian residents. Three groups were compared using chi‐square tests and logistic regression analyses: respondents who reported no injuries in the previous year, those with at least one accidental injury and those with at least one violent injury. In the bivariate analyses, the violent injury group was significantly more likely than the accidental injury and non‐injury groups to be single, widowed, separated or divorced, have more than five drinks on a usual drinking occasion, experience harmful effects of alcohol and to have used illicit drugs, such as cocaine and marijuana, and licit drugs, such as antidepressants and sleeping pills. Finally, the violent injury group was significantly more likely than those with non‐violent injuries to report that the incident was related to either their own or someone else's alcohol or drug use. In the final multiple logistic regression analysis, variables significantly associated with injuries due to criminal victimizations compared with accidental injuries were being female, single, cocaine use of the injured and substance use of someone else during the injury.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".