Current Practices in Assessing Professionalism in United States and Canadian Allopathic Medical Students and Residents
Bibliographic record
Abstract
Professionalism is a critically important competency that must be evaluated in medical trainees but is a complex construct that is hard to assess. A systematic review was undertaken to give insight into the current best practices for assessment of professionalism in medical trainees and to identify new research priorities in the field. A search was conducted on PubMed for behavioral assessments of medical students and residents among the United States and Canadian allopathic schools in the last 15 years. An initial search yielded 594 results, 28 of which met our inclusion criteria. Our analysis indicated that there are robust generic definitions of the major attributes of medical professionalism. The most commonly used assessment tools are survey instruments that use Likert scales tied to attributes of professionalism. While significant progress has been made in this field in recent years, several opportunities for system-wide improvement were identified that require further research. These include a paucity of information about assessment reliability, the need for rater training, a need to better define competency in professionalism according to learner level (preclinical, clerkship, resident etc.) and ways to remediate lapses in professionalism. Student acceptance of assessment of professionalism may be increased if assessment tools are shifted to better incorporate feedback. Tackling the impact of the hidden curriculum in which students may observe lapses in professionalism by faculty and other health care providers is another priority for further study.
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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 | 0.029 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".