Accountability in the Aftermath of OxyContin: A Network Analysis of Global Health Systems
Bibliographic record
Abstract
The opioid crisis has been recognized by scholars as one of the most pressing public health crises of our time. This crisis, responsible for more than 600,000 deaths globally and currently progressing at a rate of 100,000 deaths per year, was catalyzed by Purdue Pharmaceuticals (Purdue) and their mislabeling and mass-marketing of OxyContin, an opioid painkiller. Though Purdue is currently facing legal action in Canada and the United States, the international arm of the company, Mundipharma, is operating in over 122 countries including Brazil, China, Columbia, and Peru. To better understand the global health systems that govern transnational pharmaceutical companies like Purdue, this case study employs Actor-Network Theory and feminist Science and Technology Studies to 1) map the actor-networks involved in responding to the harms caused by Purdue; 2) explore how Purdue’s marketing narratives were constructed, mobilized and embedded in global health practices and policy environments; and 3) examine how accountability for the harms caused was enacted in and by global health systems. Data was generated from peer-reviewed and grey literature (n=36) and key informant interviews (n=18) with healthcare providers, attorneys, scholars, regulators, and industry representatives. Across three interrelated manuscripts, findings highlight the complex interplay between transnational pharmaceutical companies, regulatory bodies, public health, and global health systems. This study provides critical insight into how corporate narratives are constructed, mobilized, and embedded in health systems to shape policy and practice. Finally, this analysis identifies and critically examines the multiple co-existing social, political, and legal networks mobilized to enact accountability for the harms caused by Purdue. Through this in-depth examination of the multifaceted interactions involved in global health and pharmaceutical systems, this dissertation advances understandings of the diverse actors, relationships, and resources mobilized within global health accountability systems. These findings emphasize the need to prioritize stronger integrated and coordinated accountability mechanisms that leverage resources and strengths across sectors to safeguard public health and better hold transnational pharmaceutical companies, like Purdue, accountable for the harms they cause.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".