Ignored inequities: Critical analysis of the pre-launch development of British Columbia's “Stop Overdose” campaign
Bibliographic record
Abstract
BACKGROUND: Substance use stigma has been positioned as a major driver of drug toxicity mortality. In response, governmental and public health organizations across Canada have invested significant resources into mass media campaigns that target stigma. Many of these campaigns feature images or stories about people who use drugs (PWUD). Although stigma and drug toxicity death disproportionately impact racially and economically marginalized PWUD, these campaigns often over-represent White, middle-class individuals. This effectively ignores intersecting roles of racism and classism in the experience of stigma and drug toxicity mortality. METHODS: To investigate how this pattern of representation might occur, we examined the development process of the British Columbia (BC) Government's "Stop Overdose" anti-stigma campaign launched in 2018. We aimed to identify strategic goals, decisions, and underlying ideas that could help explain the campaign's eventual focus on White, middle-class PWUD. Through a Freedom of Information request we obtained 320 pages of documents from the BC Government outlining the real-time development, testing, and evaluation of the first wave of the campaign. We analyzed these documents using reflexive thematic analysis. RESULTS: We identified that campaign developers had a marked focus on challenging stereotypes about PWUD and humanizing PWUD, while ensuring the campaign was relevant to BC residents. To achieve these goals, campaign developers ultimately avoided images of what they deemed the inaccurately "stereotypical" marginalized drug user. Instead, they featured PWUD in more privileged social positions. By attaching labels like "co-worker" to this imagery, developers felt mainstream BC residents could relate to and have more empathy for these PWUD compared to marginalized PWUD. CONCLUSIONS: In effect, these strategies perpetuated the exclusion and dehumanization of marginalized PWUD facing disproportionate harms of the drug toxicity crisis. Since anti-stigma campaigns remain a common intervention, we highlight a need for strategic approaches informed by more critical perspectives on substance use stigma.
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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 | 0.031 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.041 | 0.021 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| 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".