Representations of Safer Opioid Supply Programs in Ontario Media Sources From 2021 to 2022: A Narrative Policy Analysis
Bibliographic record
Abstract
Toxic drug poisoning has claimed 53,821 lives since 2016 in Canada. After years of advocacy by people who use drugs and their supporters, the Canadian government funded a series of safer opioid supply (SOS) programs, where individuals who use unregulated opioids receive prescribed pharmaceutical opioids to minimize overdose risk. Twenty-six programs have been implemented in Canada since 2020, with media capturing varying positions on their implementation. Using the Narrative Policy Framework, we examined Ontario print and online news media from 2021 to 2022 to explore the narratives circulating about SOS during early implementation amid overlapping public health realities, namely the COVID-19 pandemic and the state of the toxic drug supply. We searched Canadian Newstream and conducted hand searches between February 2022 and January 2023. The retrieved media coverage was screened for: (1) a focus on SOS programs; (2) a focus on the province of Ontario; and (3) publication between 2021 and 2022. Thirty-four articles ( n = 33 newspaper articles and n = 1 magazine article) were included in our final analysis. We found a metanarrative of “crisis management,” which positioned the “policy problem” of rapidly increasing opioid-related overdoses as a crisis that could be curtailed, or conversely exacerbated, with SOS. We therefore identified two dominant narratives on SOS to be operating toward this metanarrative—SOS as the “right crisis management solution” and SOS as the “wrong crisis management solution.” Each was importantly underlined by assumptions about the relationship between the “policy problem,” the “policy solution,” and structural conditions. This article offers insights into media communication strategies and their implications for advocacy and policymaking on SOS and broader harm reduction efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".