Bibliographic record
Abstract
We described public views toward harm reduction among Canadian adults and tested a social exposure model predicting support for these contentious services, drawing on theories in the morality policy, intergroup relations, addiction, and media communication literatures. A quota sample of 4645 adults (18+ years), randomly drawn from an online research panel and stratified to match age and sex distributions of adults within and across Canadian provinces, was recruited in June 2018. Participants completed survey items assessing support for harm reduction for people who use drugs (PWUD) and for seven harm reduction interventions. Additional items assessed exposure to media coverage on harm reduction, and scales assessing stigma toward PWUD (α = .72), personal familiarity with PWUD (α = .84), and disease model beliefs about addiction (α = 0.79). Most (64%) Canadians supported harm reduction (provincial estimates = 60% - 73%). Five of seven interventions received majority support, including: outreach (79%), naloxone (72%), drug checking (70%), needle distribution (60%) and supervised drug consumption (55%). Low-threshold opioid agonist treatment and safe inhalation interventions received less support (49% and 44%). Our social exposure model, adjusted for respondent sex, household income, political views, and education, exhibited good fit and accounted for 17% of variance in public support for harm reduction. Personal familiarity with PWUD and disease model beliefs about addiction were directly associated with support (βs = .07 and -0.10, respectively), and indirectly influenced public support via stigmatized attitudes toward PWUD (βs = 0.01 and -0.01, respectively). Strategies to increase support for harm reduction could problematize certain disease model beliefs (e.g., “There are only two possibilities for an alcoholic or drug addict – permanent abstinence or death”) and creating opportunities to reduce social distance between PWUD, the public, and policy makers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.030 |
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".