Assessing the Risk of Recidivism in Physicians with Histories of Sexual Misconduct
Bibliographic record
Abstract
Physicians who engage in sexual conduct with patients usually cause serious harm and have a high rate of recidivism. Although offending physicians may lose their privilege to practice, they have the right to appeal for restoration of the license. Yet medical licensing board members do not currently have any clear standards by which to predict whether a given physician is likely to abuse again. Using New York as a paradigm, this paper offers practical, clinically based guidelines for assessing the risk of restoring an offending physician's license. These guidelines are derived from psychoanalytic theories of character, the insights of therapists who have worked with abusive physicians, and the psychiatric model of assessing dangerousness. Recognizing character patterns and psychological vulnerabilities of physicians with histories of sexual misconduct will help board members identify those who are at high risk of abusing again if their licenses are restored.
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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 | 0.001 |
| 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".