The Professionalism Mini-Evaluation Exercise: A Preliminary Investigation
Bibliographic record
Abstract
BACKGROUND: As the evaluation of professional behaviors has been identified as an area for development, the Professionalism Mini-Evaluation Exercise (P-MEX) was developed using the mini-Clinical Examination Exercise (mini-CEX) format. METHOD: From a set of 142 observable behaviors reflective of professionalism identified at a McGill workshop, 24 were converted into an evaluation instrument modeled on the mini-CEX. This instrument, designed for use in multiple settings, was tested on clinical clerks in medicine, surgery, obstetrics and gynecology, psychiatry, and pediatrics. In all, 211 forms were completed on 74 students by 47 evaluators. RESULTS: Results indicate content and construct validity. Exploratory factor analysis yielded 4 factors: doctor-patient relationship skills, reflective skills, time management, and interprofessional relationship skills. A decision study showed confidence intervals sufficiently narrow for many measurement purposes with as few as 8 observations. Four items frequently marked below expectations may be identifiers for "problem" students. CONCLUSION: This preliminary study suggests that the P-MEX is a feasible format for evaluating professionalism in clinical training.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".