Associations Between Childhood Trauma and Non-Fatal Overdose Among People Who Inject Drugs
Bibliographic record
Abstract
Introduction Although people who inject drugs (IDU) remain at a high risk of accidental overdose, interventions that address overdose remain limited. Accordingly there is a continuing need to identify psychological and social factors that shape overdose risk. Despite being reported frequently among IDU, childhood trauma has received little attention as a potential risk factor for overdose. This study aims to evaluate relationships between non-fatal overdose and five forms of childhood maltreatment among a cohort of IDU in Vancouver, Canada. Methods Data was obtained from two prospective cohorts of IDU between December 2005 and May 2013. Multivariate generalized estimating equations (GEE) were used to explore relationships between five forms of childhood trauma and non-fatal overdose, adjusting for potential confounders. Results During the study period, 1697 IDU, including 552 (32.5%) women, were followed. At baseline, 1136 (67.0%) participants reported at least one form of childhood trauma, while 4–9% reported a non-fatal overdose at each semi-annual follow-up. In multivariate analyses, physical [adjusted odds ratio (AOR): 1.36, 95% confidence interval (CI): 1.08–1.71], sexual (AOR: 1.48, CI: 1.17–1.87), and emotional abuse (AOR: 1.54, CI: 1.22–1.93) and physical neglect (AOR: 1.28, CI: 1.01–1.62) were independently associated with non-fatal overdose (all p < 0.05). Conclusions Childhood trauma was common among participants, and reporting an experience of trauma was positively associated with non-fatal overdose. These findings highlight the need to provide intensive overdose prevention to trauma survivors and to incorporate screening for childhood trauma into health and social programs tailored to IDU.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".