Health questions on medical licensure applications: effective or counterproductive? A systematic review
Bibliographic record
Abstract
Many medical regulatory authorities (MRAs) require their members and applicants to report information about their health on their medical licensure application and renewal forms. We wanted to determine whether this practice is effective in identifying physicians who have a health concern impacting their professional capacity and whether it influences members to seek treatment for their health concerns. Methods: A literature search was conducted in accordance with PRISMA publications standards. Results: From 7998 references found in all databases, after removal of duplicates and screening, five studies were included in this systematic review. None addressed the question of how effective health questions on licensure application forms are in identifying relevant health concerns. Stigma and fear of perceived ramifications of reporting mental health illness to MRAs were common reasons for physicians and medical students not seeking professional mental health care. Significance: MRAs who include health questions on their medical licensure applications should consider their effectiveness in identifying members who have health concerns that may impact their fitness to practise. Contrarily, these questions may deter members from seeking professional treatment for their own mental health. This is an important consideration, especially as burnout is prevalent among practising physicians and medical trainees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".