Representations of OxyContin in North American Newspapers and Medical Journals
Bibliographic record
Abstract
Following the approval of OxyContin (Purdue Pharma, Canada) for medical use, the media began to report the use of OxyContin as a street drug, representing the phenomenon as a social problem. Meanwhile, the pain medicine community has criticized the inaccurate and one‐sided media coverage of the OxyContin problem. The authors of this study aimed to contribute to an understanding of both sides of this controversy by analyzing the coverage of OxyContin in newspapers and medical journals. The analyses revealed inconsistent messages about the drug from physicians in the news media and in medical journals, which has likely contributed to the drug’s perception as a social problem. The authors suggest ways to address the lack of medical consensus surrounding OxyContin. The results of this study may help resolve the concerns and conflicts surrounding this drug and other opioids. BACKGROUND: There are public concerns regarding OxyContin (Purdue Pharma, Canada) and charges within the pain medicine community that media coverage of the drug has been biased. OBJECTIVE: To analyze and compare representations of OxyContin in medical journals and North American newspapers in an attempt to shed light on how each contributes to the ‘social problem’ associated with OxyContin. METHODS: Using searches of newspaper and medical literature databases, two samples were drawn: 924 stories published between 1995 and 2005 in 27 North American newspapers, and 197 articles published between 1995 and 2007 in 33 medical journals in the fields of addiction/substance abuse, pain/anesthesiology and general/internal medicine. The foci, themes, perspectives represented and evaluations of OxyContin presented in these texts were analyzed statistically. >RESULTS: Newspaper coverage of OxyContin emphasized negative evaluations of the drug, focusing on abuse, addiction, crime and death rather than the use of OxyContin for the legitimate treatment of pain. Newspaper stories most often conveyed the perspectives of law enforcement and courts, and much less often represented the perspectives of physicians. However, analysis of physician perspectives represented in newspaper stories and in medical journals revealed a high degree of inconsistency, especially across the fields of pain medicine and addiction medicine. CONCLUSION: The prevalence of negative representations of OxyContin is often blamed on biased media coverage and an ignorant public. However, the proliferation of inconsistent messages regarding the drug from physicians plays a role in the drug’s persistent status as a social problem.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".