Assessment burden by design: exploring the variability in competence by design assessment forms
Bibliographic record
Abstract
Background: The Royal College of Physicians and Surgeons of Canada's (RCPSC) Competence by Design (CBD) framework has been criticized for increasing assessment burden due to the high number of required Entrustable Professional Activity (EPA) assessments. Another contributing factor may be the inefficient design of assessment forms. We explored variability in form design to identify differences that could impact learners' and assessors' experience with CBD. Methods: = 14, 100%) RCPSC emergency medicine (EM) residency programs in Canada that had implemented CBD. Forms were divided into six sections to compare their design. The variability between form sections was described relative to RCPSC recommendations on form design. Results: EPA assessments were completed within six learning management systems. Variability was found throughout the form including the number of context variables, included milestones, milestone rating criteria, and text boxes for narrative feedback; phrasing of narrative feedback prompts, milestone descriptions, and entrustment score criterion; visual presentation of the entrustment score; arrangement of form components; and the components' selection format. The mandatory completion of form components was inconsistent. Some forms could be partially completed by residents. One form added a global performance rating scale. The number of clicks required to complete a form ranged from 12 to 47. Conclusion: We found considerable variability in the design of the EM C1 EPA assessment form. Variations that make completion more challenging could increase assessment burden. CBD programs should be aware of this and seek to optimize the design of their forms.
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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 | 0.266 | 0.564 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".