Pharmaceutical company responses to Canadian opioid advertising restrictions: A framing analysis
Bibliographic record
Abstract
The pharmaceutical industry's promotion of opioids in North America has been well-documented. Yet despite the clear consequences of improperly classifying pharmaceutical company messaging and frequently permissive approaches that allow the pharmaceutical industry to self-regulate its own advertising, there has been scarce investigation to date of how pharmaceutical industry stakeholders interpret definitions of "advertising." This study explores how variations of "marketing" and "advertising" are strategically framed by the different actors involved in the manufacturing and distribution of pharmaceutical opioids. We employed a framing analysis of industry responses to Health Canada's letter to Canadian manufacturers and distributors of opioids requesting their commitment to voluntarily cease all marketing and advertising of opioids to health care professionals. Our findings highlight companies' continuing efforts to frame their messaging as "information" and "education" rather than "advertising" in ways that serve their interests. This study also calls attention to the industry's continual efforts to promote self-regulation and internal codes of conduct within a highly permissive federal regulatory framework with little concern for violations or serious consequences. While this framing often occurring out of public sight, this study highlights the subtle means through which the industry attempts to frame their promotion strategies away from "marketing". These framing strategies have significant consequences for the pharmaceutical industry's capacity to influence healthcare professionals, patients, and the general public.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".