Reframing the Overdose Crisis: Stigma, Industry Influence, and the Politics of Abuse-Deterrent Opioids.
Bibliographic record
Abstract
Between 2013 and 2017, Canadian federal policymakers grappled with mandating abuse-deterrent formulations (ADFs) for oxycodone products as a response to the overdose crisis. Marketed as a safeguard against misuse and diversion, ADFs promised a technological fix to opioid-related harms, yet their population-level effectiveness remained contested. This study systematically analyzes federal parliamentary debates and committee hearings, identifying key arguments in framings to support or oppose ADF mandates. Proponents framed the crisis through the lens of individual misuse, positioning ADFs as pharmaceutical safeguards that protected "legitimate" patients while curbing illicit opioid use. Opponents challenged ADFs' effectiveness, highlighted Purdue Pharma's role in the crisis, and warned of unintended consequences, including shifts to more dangerous illicit markets. These discursive struggles reinforced a bifurcation between "legitimate" and "illegitimate" opioid use, shaping perceptions of responsibility, medical necessity, and the scope of appropriate intervention. Divergent framings reflected deeper ideological fissures over the etiology of the overdose crisis and who should be considered a justifiable opioid patient. By demonstrating how ADF debates entrenched a dichotomy between acceptable and unacceptable opioid use, this study advances theories of problem framing to demonstrate how policy debates actively shape regulatory paradigms and the boundaries of acceptable government intervention.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.041 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".