Timing of Income Assistance Payment and Overdose Patterns at a Canadian Supervised Injection Facility
Bibliographic record
Abstract
Background: Little is known about the relationship between timing of income assistance provision and health behaviours among injection drug users (IDU). We therefore investigated associations between income assistance provision and overdose patterns among IDU utilizing Insite, a supervised injection facility in Vancouver, Canada. Methods: Using data collected at Insite between March 2004 and December 2010, we examined trends in overdoses and drugs injected. Data were stratified by proximity to the most recent day of issue of income assistance cheques, based on dates provided by the province. Results: After adjustment for frequency of use, the risk of overdose for those injecting at Insite on the three days starting with “cheque day” was higher than for those injecting on other days (Odds Ratio [OR]=2.06; 95% Confidence Interval [CI]: 1.80–2.36, p<0.001). These associations were also significant when drug-specific overdose rates were considered. The proportion of overdoses involving exclusive opioid use was lower for events occurring around cheque day than on other days (OR=0.63; 95% CI: 0.47–0.84, p=0.002), though we observed no significant association between the proportion of overdoses involving stimulants and cheque timing (p=0.129). Conclusions: The risk of overdose among IDU utilizing Insite was significantly higher on and immediately after cheque day than during other days, and may be associated with reduced tolerance and increases in binge drug use. Alternative models of income assistance administration should be evaluated to reduce overdoses around cheque day.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".