116 Substance use in non-transport injury events: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Substance use is a key preventable risk factor for injury. However, current prevention efforts largely focus on substance use in road transport injury, with other injury causes receiving less attention. This review aims to summarise research on the prevalence of substance use in non-transport injuries. Methods Observational studies published in English after 2009 were identified by searching electronic databases, grey literature and reference lists. Eligible studies (1) examined individuals ≥15 years presenting to a hospital or forensic centre following a non-transport related injury; and (2) included an objective toxicology measure. Meta-analyses were performed where appropriate. Results Two reviewers independently screened 11,413 records and 2,078 full-text articles. Of these, 122 studies were included. Most studies reported on alcohol (n=114, 93%) with varying prevalence (falls: 4–57%, assaults: 10–71%, firearms: 21–42%, penetrating injuries: 9–25%, suicides: 20–50%). Less research examined other drugs including cannabis (n=18), amphetamines (n=14), cocaine (n=14), opioids (n=10) and benzodiazepines (n=7). In meta-analyses, 37% of fall-related injuries involved any substance use, 35% of firearm injuries involved alcohol and 31% of suicides involved alcohol. Conclusion Substance use is prevalent across injury causes. However, prevalence varied between studies and inadequate reporting of study methods often impaired comparison of these results. Learning Outcomes Given the prevalence of substance use in non-transport injuries, there is need for targeted injury prevention and harm minimisation strategies addressing these injury causes. Future research should focus on the role of drugs other than alcohol in specific injury causes and would benefit from improved reporting of toxicology testing methods.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".