Developing a dashboard for faculty development 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 frequent assessments of entrustable professional activities (EPAs). Faculty struggle to provide helpful feedback and assign appropriate entrustment scores. CBME faculty development initiatives rarely incorporate teaching metrics. Dashboards could be used to visualize faculty assessment data to support faculty development. METHODS: Using a design-based research process, we identified faculty development needs related to CBME assessments and designed a dashboard containing elements (data, analytics, and visualizations) meeting these needs. Data was collected within the emergency medicine residency program at the University of Saskatchewan through interviews with program leaders, faculty development experts, and faculty participating in development sessions. Two investigators thematically analyzed interview transcripts to identify faculty needs that were audited by a third investigator. The needs were described using representative quotes and the dashboard elements designed to address them. RESULTS: Between July 1, 2019 and December 11, 2020 we conducted 15 interviews with nine participants (two program leaders, three faculty development experts, and four faculty members). Three needs emerged as themes from the analysis: analysis of assessments, contextualization of assessments, and accessible reporting. We addressed these needs by designing an accessible dashboard to present contextualized quantitative and narrative assessment data for each faculty member. CONCLUSIONS: We identified faculty development needs related to EPA assessments and designed dashboard elements to meet them. The resulting dashboard was used for faculty development sessions. This work will inform the development of CBME assessment dashboards for faculty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".