“It Takes More than a Pill to Kill”: Professional Self-Control in the Opioid Epidemic
Bibliographic record
Abstract
How professions regulate misconduct within their own ranks has come under increased scrutiny. Within the medical profession, the question of the efficacy of self-control has become especially pertinent considering the opioid crisis, where physicians’ over-prescribing behavior has been linked to overdose deaths. Existing theory suggests that professions may be lenient towards misconduct behind closed doors, but will discipline members guilty of misconduct when their behavior publicly casts the profession in a negative light. However, this latter insight has not been empirically tested. To understand how a profession regulates misconduct when the misconduct attracts increased public scrutiny, we gained access to a U.S. State’s Medical Board’s internal deliberations regarding how to discipline physicians guilty of overprescribing opioids. We found that even in the most egregious cases of physicians overprescribing opioids, the Board allowed most guilty physicians to keep their medical licenses. Our analysis uncovers how this leniency was produced in spite of heightened legislative and media attention on addressing the opioid epidemic in the State. We show that leniency was produced through three mechanisms: the bureaucracy within which the Board was enmeshed, a professional belief in rehabilitation, and personal feelings of sympathy towards offending physicians.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".