Junkie or patient? media’s portrayal of opioid harm reduction strategies in Nova Scotia
Bibliographic record
Abstract
Safe supply programs rolled across Canada amidst two public health emergencies: the opioid crisis and COVID-19 pandemic.Earlier research demonstrated safe supply's efficacy, but confusing messages rooted in a history of racism, criminalization and stigmatization limited access to this life saving program.Harm reduction interventions reduce mortality and improve health outcomes for people who use substances.Criminalization confuses messages surrounding treatment; creates an unsafe illegal market; and fails to eradicate substance use in Canada.Media plays an influential role in shaping public perceptions.This study describes volume, content and themes from Nova Scotia news media sources that discuss safe supply between 2018 and 2022.Searches of three English-language news media sources from Nova Scotia identified 41 articles, coded for type, tone, topic, harm reduction intervention and thematically analyzed.Volume of coverage increased over time, which coincided with the COVID-19 outbreak and implementation of safe supply programs.Changes in narrative framing and use of stigmatizing language were also observed.No longer understood as a political or criminal issue, Nova Scotia news media content frames safe supply as a health and social justice issue.News media sources in Nova Scotia are not directly contributing to the stigmatization of people who use substances.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".