Deconstructing the ‘cheque effect’: short‐term changes in injection drug use after receiving income assistance and associated factors
Bibliographic record
Abstract
BACKGROUND AND AIMS: Disbursement of income assistance has been temporally associated with intensified drug use and related harms (coined the 'cheque effect'). However, relationships to injection drug use (IDU) remain understudied. We examined short-term 'cheque effects' and associated factors among people who inject drugs (PWID). DESIGN: Cross-sectional analysis nested within a cohort study. SETTING: Montreal, Quebec, Canada. PARTICIPANTS: PWID receiving income assistance, with no employment income. A total of 613 PWID (median age 41, 83% male) contributed 3269 observations from 2011 to 2017. MEASUREMENTS AND METHODS: At each cohort visit, an interviewer-administered questionnaire captured retrospective reports of injection-related behaviour during the 2-day periods (i) before and (ii) including/after receiving last month's income assistance payment (number of injections; drugs injected; any receptive syringe-sharing). The relative likelihood (odds) and magnitude (rate) of an increase in injection frequency ('cheque effect') were estimated in relation to social and behavioural factors using logistic and negative binomial regression in a covariate-adjusted two-part model. FINDINGS: Prevalence of IDU and syringe-sharing were, respectively, 1.80 and 2.50 times higher in the days following versus preceding cheque receipt (P < 0.001). Among people with past-month IDU, most observations showed increased injection frequency (52%) or no change in injection frequency (44%). The likelihood of a 'cheque effect' was positively associated with cocaine injection [versus injection of other substances, odds ratio (OR) = 2.639, 95% confidence interval (CI) = 2.04-3.41], unstable housing (OR = 1.272, 95% CI = 1.03-1.57) and receiving opioid agonist therapy (OR =1.597, 95% CI = 1.27-2.00) during the same month. Magnitude of the 'cheque effect' was positively associated with cocaine injection [rate ratio (RR) = 1.795, 95% CI = 1.43-2.16], unstable housing (RR = 1.198, 95% CI = 1.02-1.38) and frequent injection (RR = 2.938, 95% CI = 2.43-3.44), but inversely associated with opioid agonist therapy (RR = 0.817, 95% CI = 0.68-0.95) and prescription opioid injection (RR = 0.794, 95% CI = 0.66-0.93). CONCLUSION: Among people who inject drugs in Montreal, Canada, injection drug use and receptive syringe-sharing appear to be more prevalent in the 2 days after versus before receiving income assistance. The odds and rate of individual-level increases in injection frequency appear to be positively associated with cocaine injection (versus injection of other substances) and unstable housing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".