Developing a dashboard to meet the needs of residents in a competency-based training program: A design-based research project
Bibliographic record
Abstract
BACKGROUND: Canadian specialty programs are implementing Competence By Design, a competency-based medical education (CBME) program which requires frequent assessments of entrustable professional activities. To be used for learning, the large amount of assessment data needs to be interpreted by residents, but little work has been done to determine how visualizing and interacting with this data can be supported. Within the University of Saskatchewan emergency medicine residency program, we sought to determine how our residents' CBME assessment data should be presented to support their learning and to develop a dashboard that meets our residents' needs. METHODS: We utilized a design-based research process to identify and address resident needs surrounding the presentation of their assessment data. Data was collected within the emergency medicine residency program at the University of Saskatchewan via four resident focus groups held over 10 months. Focus group discussions were analyzed using a grounded theory approach to identify resident needs. This guided the development of a dashboard which contained elements (data, analytics, and visualizations) that support their interpretation of the data. The identified needs are described using quotes from the focus groups as well as visualizations of the dashboard elements. RESULTS: Resident needs were classified under three themes: (1) Provide guidance through the assessment program, (2) Present workplace-based assessment data, and (3) Present other assessment data. Seventeen dashboard elements were designed to address these needs. CONCLUSIONS: Our design-based research process identified resident needs and developed dashboard elements to meet them. This work will inform the creation and evolution of CBME assessment dashboards designed to support resident learning.
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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 | 0.005 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".