Determinants of overdose incidents among illicit opioid users in 5 Canadian cities
Bibliographic record
Abstract
BACKGROUND: Drug overdose is a major cause of death and illness among illicit drug users. Previous research has indicated that most illicit drug users experience nonfatal overdoses and has suggested a variety of factors that are associated with risk of overdose. In this study, we examined the occurrence of and the factors associated with nonfatal overdoses within a Canadian sample of illicit opioid users not enrolled in treatment at the time of study recruitment. METHODS: Interviewers used a standard questionnaire to collect data on sociodemographic characteristics, drug use, health and health care, experience in the criminal justice system and treatment for drug problems; they also performed standard assessments for mental health and infectious disease. The association between overdose and sociodemographic and drug-use factors was examined with chi(2) and t test analyses; marginally significant variables were examined with logistic regression to determine independent effects. RESULTS: A total of 679 subjects were interviewed; 651 provided answers sufficient for this analysis. One hundred and twelve (17.2%) of the 651 respondents reported an overdose episode in the previous 6 months. In the logistic regression analysis (after adjustment for sociodemographic factors), homelessness, noninjection use of hydromorphone in the past 30 days and involvement in drug treatment in the past 12 months were predictors of overdose (p < 0.05). INTERPRETATION: Overdose poses a considerable health risk for illicit opioid users. We found that a diverse set of factors was associated with overdose episodes. Prevention efforts will likely be more effective if they can be directed to specific causal factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 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) | 0.002 | 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".