Socio-demographic and substance use characteristics of unintentional injuries among Nunavik youth
Bibliographic record
Abstract
This study described the distribution of unintentional injuries among Inuit youth in Nunavik, Quebec, Canada, and examined the relationship between socio-demographic factors, substance use and unintentional injuries.A cross-sectional study design was used on data collected for the Nunavik Child Development Study (2013-2015) among eligible youth aged 16 to 21 years old. Unintentional injury occurrence and causes (last 12 months) were assessed through individual interviews. A multivariate logistic regression model tested the relationship between socio-demographic, substance use variables and unintentional injury occurrence.Among the 199 youth who participated (94% response rate), thirty youth reported being unintentionally injured in the past 12 months , of which 50% were female. All-terrain vehicle collisions were the most frequent injuries reported (23%). The odds of being injured decreased by 62% for youth who were currently employed compared to those who were unemployed, adjusting for other socio-demographic variables (p-value = 0.04). Heavy alcohol drinking in the past 12 months was not significantly associated with unintentional injury.This study highlights the burden of unintentional injuries among Nunavik youth and the need for future work to explore additional and diverse variables that may prevent or contribute to injuries in order to inform culturally and developmentally-appropriate injury prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".