Upvoting stigma? Analyzing themes in substance use stigma within Canadian subreddits
Bibliographic record
Abstract
BACKGROUND: Public perceptions of substance use are important drivers of drug policy, resource allocation, and stigma worldwide. Despite increasing adoption of public health-oriented approaches in some countries, stigma remains a pervasive barrier to harm reduction and treatment engagement. This study examines themes and prevalence of stigmatizing substance use discourse from Canadian city subreddits, providing insights relevant to countries considering policy reform and stigma reduction strategies. METHODS: We collected illicit substance-related posts and comments from major Canadian city subreddits (e.g. r/Toronto, r/Winnipeg). A zero-shot learning approach, utilizing the BART-large model, classified posts as stigmatizing or non-stigmatizing. Posts meeting a ≥ 90 % confidence threshold for stigma underwent qualitative thematic analysis to identify narratives shaping stigmatizing discussions of illicit substance use. RESULTS: Of the substance-related posts analyzed, 14.4 % contained stigma (with ≥90 % confidence). Stigma was most frequently associated with crack (18 %), cocaine (18 %), opioids (15 %), and methamphetamine (14 %). The thematic analysis identified four dominant narratives: Perceived Enabling versus Effective Care in the Evaluation of Harm Reduction (37.5 %), Punitive Criminalization and Law Enforcement (23.2 %), Exclusion in the Name of Community Safety (22.5 %), and Taxpayer Resentment and Public Spending (18.6 %). DISCUSSION: Findings highlight the prevalence and themes of illicit substance use stigma within discussions on Canadian city subreddits on Reddit. Stigmatizing language and attitudes may contribute to resistance against harm reduction measures and punitive policy shifts. Addressing these narratives through public education, evidence-based policy reform, and stigma reduction initiatives is essential for fostering a more inclusive approach to substance use health.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".