Development and Validation of the McMaster Prescribing Competency Assessment for Medical Trainees (MacPCA).
Bibliographic record
Abstract
BACKGROUND: Prescribing is an essential skill for all physicians, built on knowledge of clinical pharmacology, therapeutics and toxicology across the life cycle. The decline in organized clinical pharmacology training in medical schools, combined with an expanding pharmacopeia and increasing complexity of patient care, makes prescribing competency difficult for medical students to master. OBJECTIVES: To develop and validate the McMaster Prescribing Competency Assessment (MacPCA), an online tool suitable for evaluating clinical pharmacology knowledge and prescribing skills of medical trainees in Canada. METHODS: The MacPCA was developed using an online examination platform scalable to multiple sites across Canada. Questions represented 8 domains of safe and effective prescribing with level of difficulty aimed at a final year medical student. Validation assessment concentrated on face and construct validity. RESULTS: 58 participants (7, 12 and 21 medical students in Years 1, 2, and 3, respectively and 8 undergraduate controls) were recruited. Mean scores were 31% (SD 13.6), 46% (SD 14.9), 75% (SD 8.3) and 81% (SD 10.5) for the controls, Year 1, Year 2, and Year 3 (final year) students, respectively. Combined Year 2/Year 3 scores were significantly better than control/Year 1 scores (p<0.0001). Final year student feedback indicated the test was fair, clear and unambiguous, aimed at the right level, with sufficient time for completion. CONCLUSIONS: The MacPCA demonstrated good face validity and successfully discriminated between upper year medical students and their junior colleagues. Further expansion of testing and validation is warranted.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".