Regulation in Need of Therapy? Analysis of Regulatory Decisions Relating to Impaired Doctors from 2010 to 2020.
Bibliographic record
Abstract
Doctors' mental wellbeing is a critical public health issue. Rates of depression, anxiety, and substance use are higher than in the general population. Regulating unwell doctors who pose a public risk is challenging, yet there is little research into how medical regulators balance the need to protect the public from harm against the benefits of supporting and rehabilitating the unwell doctor. We analysed judgments from Australia, New Zealand, Ireland, United Kingdom, Ontario, and Singapore between 2010 and 2020 relating to impaired doctors. We found similarities in how decision-makers conceptualise impairment, how they disentangle impairment from associated conduct or performance complaints, and how regulatory principles and sanctions are applied. However, compared to other jurisdictions, Australian courts and tribunals tended to prioritise deterrence above the rehabilitation of the impaired doctor. Supporting impaired doctors' recovery, when appropriate, is critical to public protection and patient safety.
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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 | 0.022 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".