Nonfatal overdose from alcohol and/or drugs among a sample of recreational drug users
Bibliographic record
Abstract
The purpose of this study was to examine nonfatal overdose events experienced among a sample of recreational drug users. We sought to determine predictors of nonfatal overdose from alcohol and/or drugs among a sample of recreational drug users. In addition, we examined the substance(s) used at the last overdose event. Methods: Participants were 637 recreational illicit drug users (had used illicit drugs other than marijuana, in a club or party setting), aged 19 or older, from Victoria or Vancouver, British Columbia, Canada. Data were obtained in structured interviews conducted from 2008 to 2012 as part of the Canadian Recreation Drug Use Survey (CRDUS). Results: In the 12 months prior to interview, 19.3% (n = 123) of the participants had experienced an overdose. In multivariate analysis, younger age, unstable housing, and usually consuming eight or more drinks containing alcohol, when drinking, significantly increased overdose risk. In addition, polysubstance use was reported by 67.5% (n = 83) participants at their last overdose event. Conclusions: Intervention and prevention measures seeking to reduce overdoses among recreational drug users should not only address illicit drug use but also alcohol and polysubstance use. In addition, measures may target those who usually consume high amounts of alcohol when drinking are younger and who experience housing instability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".