Service provision to physicians with mental health and addiction problems
Bibliographic record
Abstract
PURPOSE OF REVIEW: Physicians are reluctant to ask for help when they suffer from substance use disorders and/or other mental illnesses (i.e. when they become 'sick doctors'). This can result in greater morbidity/mortality and may lead to significant problems in medical practice. This review aims to describe the nature and development of programs that specifically treat sick doctors [Physician Health Programs (PHPs)]. RECENT FINDINGS: PHPs were first developed in the United States in the late 1970s. The purpose was to identify and treat physicians with problems resulting from mental health issues, mainly substance use disorders. Since then, other PHPs have been developed in Canada, Australia, and the United Kingdom, trying to reach sick doctors, offering counseling or other preventive interventions when needed. New models to help sick doctors, such as the Spanish PHP, were also developed. Counseling and support services for sick doctors have been implemented elsewhere in Europe (e.g. Norway and Switzerland). SUMMARY: PHPs provide interventions specifically designed for physicians and other medical professionals with substance use and other mental health problems. The balance between guaranteeing safe practice and yet encouraging all physicians to ask for help when in trouble raises questions regarding how these programs should be designed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".