Networked narratives: Examining how Purdue Pharmaceuticals shaped public health policy and practice
Bibliographic record
Abstract
In 1996, Purdue Pharmaceutical's (Purdue) launched OxyContin, an opioid painkiller, with the largest marketing strategy in pharmaceutical history. Literature has now established that Purdue's marketing of OxyContin was a root cause of the current opioid crisis, responsible for over 600,000 deaths in and beyond North America. Guided by actor-network theory, this study conducted a document analysis and key informant interviews ( n = 18) to examine the processes through which Purdue constructed, mobilized, and embedded their marketing narratives in global health practice and policy environments. The data generated reveals Purdue's narrative, conveying that opioids are both safe and necessary for the treatment of pain, was constructed as a means of increasing the prescription of OxyContin, and therefore, shareholder profits. As reports of opioid dependence and overdose deaths began to rise in the early 2000s, Purdue added a second component to their narrative: that any misuse of prescription opioids was due to the personal failings of “drug addicts” rather than the company's product or actions. This narrative was then mobilized through recruiting key actors, including public relation firms and medical professionals, to reach physicians, policymakers, and the public. Through disseminating their narrative and embedding it in news articles, academic scholarship, and educational resources, Purdue successfully increased opioid acceptability and delayed policy responses to the opioid crisis. By embedding their narrative in public health fora, Purdue continues to have significant implications for global health policies and medical practices. By better understanding how Purdue mobilized and enrolled actors to deflect accountability from their corporate malfeasance, this study provides insight into how certain actors can exert disproportionate influence over regulatory, medical, and public domains.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| 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".