Teaching Professionalism in Postgraduate Medical Education: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This systematic review sought to summarize published professionalism curricula in postgraduate medical education (PGME) and identify best practices for teaching professionalism. METHOD: Three databases (MEDLINE, Embase, ERIC) were searched for articles published from 1980 through September 7, 2017. English-language articles were included if they (1) described an educational intervention addressing professionalism, (2) included postgraduate medical trainees, and (3) evaluated professionalism outcomes. RESULTS: Of 3,383 articles identified, 50 were included in the review. The majority evaluated pre- and posttests for a single group (24, 48%). Three (6%) were randomized controlled trials. The most common teaching modality was small-group discussions (28, 56%); other methods included didactics, reflection, and simulations. Half (25, 50%) used multiple modalities. The professionalism topics most commonly addressed were professional values/behavior (42, 84%) and physician well-being (23, 46%). Most studies measured self-reported outcomes (attitude and behavior change) (27, 54%). Eight (16%) evaluated observed behavior and 3 (6%) evaluated patient outcomes. Of 35 studies that evaluated statistical significance, 20 (57%) reported statistically significant positive effects. Interventions targeting improvements in knowledge were most often effective (8/12, 67%). Curriculum duration was not associated with effectiveness. The 45 quantitative studies were of moderate quality (Medical Education Research Study Quality Instrument mean score = 10.3). CONCLUSIONS: Many published curricula addressing professionalism in PGME are effective. Significant heterogeneity in curricular design and outcomes assessed made it difficult to synthesize results to identify best practices. Future work should build upon these curricula to improve the quality and validity of professionalism teaching tools.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".