Do Medical Licensing Questions on Health Conditions Pose a Barrier to Physicians Seeking Treatment? A Literature Review
Bibliographic record
Abstract
Physician health is strongly connected to patient health outcomes such that barriers to seeking help and medical care for impaired physicians may compromise patient safety and quality of care. It is important to understand and identify barriers that may reduce the likelihood of physicians seeking help. Using medical licensure questions that necessitate self-reporting of health conditions is one of the ways regulatory bodies such as the College of Physicians and Surgeons of Alberta (CPSA) seeks to protect the public and ensure physician competency. The objective of this paper is to review the current body of literature on the impact of these medical licensure questions on physician health-seeking behavior as well as patient care. Five online databases (Scopus, APA PsychINFO, Web of Science, PubMed, and MEDLINE) were searched using combined key terms to identify relevant articles. Based on the inclusion and exclusion criteria, nine primary quantitative studies were selected. Results suggest that licensure applications with questions on previous impairments and mental health condition acts as both a barrier to reporting and to seeking care. These findings highlight the need for further research in examining the utility of health licensure questions in identifying impaired physicians and their impact on the quality of patient care.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".