How the Suboxone Education Programme presented as a solution to risks in the Canadian opioid crisis: a critical discourse analysis
Bibliographic record
Abstract
OBJECTIVES: Pharmaceutical industry involvement in medical education, research and clinical practice can lead to conflicts of interest. Within this context, this study examined how the 'Suboxone Education Programme', developed and delivered by a pharmaceutical company as part of a federally regulated risk management program, was presented as a solution to various kinds of risks relating to opioid use in public documents from medical institutions across Canada. SETTING: These documents were issued during the Canadian opioid crisis, a time when the involvement of industry in health policy was being widely questioned given industry's role in driving the overprescribing of opioid analgesics and contributing to population-level harms. DESIGN: A critical discourse analysis of 69 documents collected between July 2020 and May 2021 referencing the Suboxone Education Program spanning 13 years (2007-2021) from medical, nursing and pharmacy institutions sourced from every Canadian province and territory. Discursive themes were identified through iterative and duplicate analyses using a semistructured data extraction instrument. RESULTS: Documents characterised the Programme as addressing iatrogenic risks from overprescribing opioid analgesics, environmental risks from a toxic street drug supply and pharmacological risks relating to the dominant therapeutic alternative of methadone. The programme was identified as being able to address these risks by providing mechanisms to surveil healthcare professionals and to facilitate the prescribing of Suboxone. Medical institutions legitimised the Suboxone Education Programme by lending their regulatory, epidemiological and professional authority. CONCLUSIONS: Addressing risk is considered as a central, moral responsibility of contemporary healthcare services. In this case, moral imperatives to address opioid crisis-related risks overrode other ethical concerns regarding conflicts of interest between industry and public welfare. Failing to address these conflicts potentially imperils efforts of mitigating population health harms by propagating an important driving force of the opioid crisis.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.036 | 0.045 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".