Key considerations in planning and designing programmatic assessment in competency-based medical education
Bibliographic record
Abstract
Programmatic assessment as a concept is still novel for many in clinical education, and there may be a disconnect between the academics who publish about programmatic assessment and the front-line clinical educators who must put theory into practice. In this paper, we clearly define programmatic assessment and present high-level guidelines about its implementation in competency-based medical education (CBME) programs. The guidelines are informed by literature and by lessons learned from established programmatic assessment approaches. We articulate five steps to consider when implementing programmatic assessment in CBME contexts: articulate the purpose of the program of assessment, determine what must be assessed, choose tools fit for purpose, consider the stakes of assessments, and define processes for interpreting assessment data. In the process, we seek to offer a helpful guide or template for front-line clinical educators. We dispel some myths about programmatic assessment to help training programs as they look to design-or redesign-programs of assessment. In particular, we highlight the notion that programmatic assessment is not 'one size fits all'; rather, it is a system of assessment that results when shared common principles are considered and applied by individual programs as they plan and design their own bespoke model of programmatic assessment for CBME in their unique context.
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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 | 0.196 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.017 |
| 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; 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".