Developing a dashboard for program evaluation in competency-based training programs: a design-based research project
Bibliographic record
Abstract
Background: Canadian specialist residency training programs are implementing a form of competency-based medical education (CBME) that requires the assessment of entrustable professional activities (EPAs). Dashboards could be used to track the completion of EPAs to support program evaluation. Methods: Using a design-based research process, we identified program evaluation needs related to CBME assessments and designed a dashboard containing elements (data, analytics, and visualizations) meeting these needs. We interviewed leaders from the emergency medicine program and postgraduate medical education office at the University of Saskatchewan. Two investigators thematically analyzed interview transcripts to identify program evaluation needs that were audited by two additional investigators. Identified needs were described using quotes, analytics, and visualizations. Results: Between July 1, 2019 and April 6, 2021 we conducted 17 interviews with six participants (two program leaders and four institutional leaders). Four needs emerged as themes: tracking changes in overall assessment metrics, comparing metrics to the assessment plan, evaluating rotation performance, and engagement with the assessment metrics. We addressed these needs by presenting analytics and visualizations within a dashboard. Conclusions: We identified program evaluation needs related to EPA assessments and designed dashboard elements to meet them. This work will inform the development of other CBME assessment dashboards designed to support program evaluation.
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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 | 0.246 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".