A critical content analysis of media reporting on opioids: The social construction of an epidemic
Bibliographic record
Abstract
BACKGROUND: The 2000s have seen a proliferation of media reporting about opioid use in North America. Given the significant role that popular media plays in shaping the public's perceptions and understandings of the issues that it represents, analysing the content of this media coverage can help understand public discourse about opioid use. METHODS: We conducted a critical content analysis of Canadian newsprint media reporting on opioids using a sociological lens. We performed a qualitative thematic analysis of these texts, coding 826 articles and applying a critical discourse analysis in our interpretation of the findings. FINDINGS: Our analysis showed a slow transition from a conversation primarily about clinical pain care towards a discussion of criminality, especially the increasingly fluidity of boundaries between prescription opioid use and the illegal drug trade. Patients tend to be dichotomized as either innocently following physician prescriptions or drug-seeking, as an aspect of lives characterized by addiction and street crime. These depictions map onto characterizations of physicians as naively following pharmaceutical industry advice or becoming irrelevant once criminality is introduced. DISCUSSION: The social construction of the opioid epidemic polarizes individuals as good or bad with little attention paid to underlying institutional interests both in the creation of the problem or in the solutions that are proposed. We show that as concerns about harms from opioids become more pronounced, the narrative shifts to home in on illicit street-use with a corresponding uptake of stigmatizing references to so-called addicts. Concurrently, most references to the pharmaceutical industry disappear from view. This framing of the problem defines the kinds of solutions that then seem natural. For example, increased criminalization is suggested for people who use drugs and stigmatizing those who suffer with chronic pain becomes a higher priority than implementing safer and more effective therapies for managing their pain.
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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 | 0.032 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".