Evaluation of an innovative learner-centred assessment program for family medicine residency training
Bibliographic record
Abstract
Background/Purpose The Calgary Family Medicine Residency Program introduced its new “Triple-C” competency-based curriculum in 2012 and concomitantly developed and implemented an innovative competency-based assessment program based on current best-practice recommendations. This new assessment program utilizes multiple assessment data including field notes, progress reviews, self-assessments and Entrustable Professional Activities (EPA's). This 2-phase project studies the impact of the implementing this new assessment program at both Resident and Preceptor levels (Phase I) and also the evidence for the reliability, validity and feasibility of the assessment methods chosen (Phase 2). Methods In Phase I of this study, a total of 10 Residents and 16 Preceptors were interviewed to explore their experiences of the new assessment program. Study participants were selected using a purposeful sampling method and interviews completed using a semi-structured interview guide. Interviews were recorded and subsequently transcribed verbatim for thematic analysis. Data from the Phase 1 interviews was used to generate the Phase 2 program-wide survey instrument for use by all Preceptors and Residents. Results Qualitative data from the Phase 1 thematic analysis will be presented. Results include i) implementation issues –barriers and facilitators, ii) Resident and Preceptor perceptions around educational benefits of the new assessment program and its value in promoting learning. Preliminary quantitative data from Phase 2 will also be presented. Conclusions The results this study will help our understanding of how a multi-method, workplaced based assessment program impacts learners and preceptors, and to what extent both learners and teachers accept the legitimacy of these processes.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".