Elevated depressive symptoms and adolescent injury: examining associations by injury frequency, injury type, and gender
Bibliographic record
Abstract
BACKGROUND: Key risk factors for adolescent injury have been well documented, and include structural, behavioural, and psychosocial indicators. While psychiatric distress has been associated with suicidal behaviour and related self-harm, very little research has examined the role of depression in shaping adolescent injury. This study examines the association of elevated depressive symptoms with injury, including total number of injuries and injury type. Gender differences are also considered. METHODS: Data were drawn in 2010-11 from a representative sample of 2,989 high school students (14 to18 years of age) from Nova Scotia, Canada. Self-reported injury outcomes were examined using the 17-item Adolescent Injury Checklist, which captures past six-month injuries. Elevated depressive symptoms were assessed using the Centers for Epidemiological Studies Depression scale. Associations of elevated depressive symptoms with total number of injuries were estimated with negative binomial regression, while associations with specific injury types were estimated with logistic regression. Analyses were conducted in 2012. RESULTS: Adolescents with elevated depressive symptoms experienced a 40% increase in the total number of injury events occurring in the past six months. The association of elevated depressive symptoms with injury was consistent across injury type; violence-related (OR 2.21, 95% CI 1.61 to 3.03), transport-related (OR 1.53, 95% CI 1.10 to 2.13), and unintentional injuries (OR 1.65, 95% CI 1.20 to 2.27). Gender differences were also observed. CONCLUSION: Elevated depressive symptoms play a role in shaping adolescent injury. Interventions aimed at reducing adolescent injury should look to minimize psychosocial antecedents, such as poor mental health, that put adolescents at an elevated risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".