Competency-based achievement system: using formative feedback to teach and assess family medicine residents' skills.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Family medicine residency programs require innovative means to assess residents' competence in "soft" skills (eg, patient-centred care, communication, and professionalism) and to identify residents who are having difficulty early enough in their residency to provide remedial training. OBJECTIVE OF PROGRAM: To develop a method to assess residents' competence in various skills and to identify residents who are having difficulty. PROGRAM DESCRIPTION: The Competency-Based Achievement System (CBAS) was designed to measure competence using 3 main principles: formative feedback, guided self-assessment, and regular face-to-face meetings. The CBAS is resident driven and provides a framework for meaningful interactions between residents and advisors. Residents use the CBAS to organize and review their feedback, to guide their own assessment of their progress, and to discern their future learning needs. Advisors use the CBAS to monitor, guide, and verify residents' knowledge of and competence in important skills. CONCLUSION: By focusing on specific skills and behaviour, the CBAS enables residents and advisors to make formative assessments and to communicate their findings. Feedback indicates that the CBAS is a user-friendly and helpful system to assess competence.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".