The effects of academic unprofessional behaviour on disciplinary action by medical boards: Systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective The objective of this study is to evaluate the association of disciplinary actions by regulatory councils and unprofessional behaviour during medical graduation. Methods A search strategy was developed using the terms: ‘physicians’, ‘disciplinary action’, ‘education’, ‘medical’, ‘undergraduate’ and their synonyms, subsequently applied to the electronic databases MEDLINE, Embase, Cochrane Library, LILACs and grey literature, with searches up to November 2023. The risk of bias was assessed using the Newcastle‐Ottawa scale and statistical analysis was performed using the RevMan software. Results A total of 400 studies were found in the databases, and 15 studies were selected for full‐texting reading. Four studies met the inclusion criteria and were included, bringing together a total of 3341 evaluated physicians. Three studies were included in the meta‐analysis, showing a greater chance of disciplinary actions among physicians who exhibited unprofessional behaviour during medical graduation (OR: 2.54; 95%CI: 1.87–3.44; I 2 : 0%; P < 0.0001; 3077 participants; physicians with disciplinary action: 107/323; control physicians: 222/2754). Conclusions There is a statistically significant association between unprofessional behaviour during medical undergraduate study and subsequent disciplinary actions by Medical Councils. The tools for periodic assessments of student behaviour during undergraduate studies can be a perspective for future studies aimed at reducing disciplinary actions among physicians.
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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".