Accountability in global health systems: insights from a network analysis of Purdue Pharmaceuticals
Bibliographic record
Abstract
Beginning in 1996, Purdue Pharmaceuticals (Purdue) knowingly mislabeled and mass marketed OxyContin (oxycodone), an opioid painkiller, catalyzing the opioid crisis which has been responsible for more than 600 000 deaths in and beyond North America. This case is an extreme example of how transnational pharmaceutical companies prioritize shareholder profits over public wellbeing. As such, the field of global health faces the critical challenge of better understanding how transnational pharmaceutical companies, like Purdue, can be held to account for the harms they cause. Within the framework of Actor-Network Theory, a sociomaterial approach to analyzing complex networks, this case study uses key informant interviews (n = 18) to examine how accountability is taken up in and by global health systems in response to the harms caused by Purdue. Findings highlight the multiple co-existing versions of accountability enacted within global health systems organized as three separate but interrelated networks: social accountability, political accountability, and legal accountability. Though often interconnected, these diverse networks mobilized distinct tools, resources, and strategies, such as news articles, scholarly literature, and policy guidelines, to construct and stabilize enactments of accountability. Through this in-depth examination of the complex interactions involved in global health and pharmaceutical systems, this study offers a nuanced understanding of the diverse actors mobilized and the unique strengths leveraged within and by accountability networks. Further, in examining these networks' differences, interconnectedness, and peculiarities, we broaden the scope of how accountability is defined, conceptualized, and operationalized in global health systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".