Association between posttraumatic stress disorder and nonfatal drug overdose.
Bibliographic record
Abstract
OBJECTIVE: North America is in the midst of a growing drug overdose crisis. While prescription opioid misuse and synthetic opioids such as fentanyl have been implicated in the overdose crisis, less attention has been given to the role that posttraumatic stress disorder (PTSD) may play in this crisis. As such, this study sought to examine the relationship between PTSD and risk of nonfatal overdose among people who use drugs (PWUD). METHOD: Data were derived from three prospective cohorts of PWUD in Vancouver, Canada. For each participant, PTSD was assessed using the PTSD Checklist for the DSM-5. Multivariate logistic regression analysis was used to estimate the relationship between PTSD and nonfatal overdose, adjusting for potential confounders. RESULTS: Between 2016 and 2018 among 1,059 PWUD, including 363 (34%) nonmale participants, 171 (16%) experienced a nonfatal drug overdose in the past 6 months, and 414 (39%) met criteria for a provisional PTSD diagnosis. In multivariate analysis, PTSD (adjusted odds ratio = 1.98, 95% confidence interval [1.4, 2.79]) remained independently associated with nonfatal overdose after adjustment for a range of confounders. CONCLUSIONS: Among participants in these community-recruited cohorts of PWUD, having a provisional PTSD diagnosis nearly doubled the risk of nonfatal overdose. The findings from this study support the need to incorporate a trauma-informed approach within the current overdose prevention framework. Education and training relating to trauma and PTSD should be prioritized for health care professionals who work with and treat PWUD. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".