Assessing competencies using milestones along the way
Bibliographic record
Abstract
This paper presents perspectives and controversies surrounding the use of milestones to assess competency in outcomes-based medical education. Global perspectives (Canada, Europe, and the United States) and developments supporting their rationales are discussed. In Canada, there is a significant movement away from conceptualizing competency based on time, and a move toward demonstration of specific competencies. The success of this movement may require complex (rather than reductionist) milestones that reflect students' progression through complexity and context and a method to narrate their journey. European countries (United Kingdom, France, and Germany) have stressed the complexity associated with time and milestones for medical students to truly achieve competence. To meet the changing demands of medicine, they view time as actually providing students with knowledge and exposure to achieve various milestones. In the United States, milestones are based on sampling throughout professional development to initiate lifelong learning. However, the use of milestones may not imply overall competence (reductionism). Milestones must be developed alongside outcomes-based curriculum with use of faculty and competency committees. The perspectives outlined in this paper underscore emerging challenges for implementing outcomes-based medical education and call for new conceptualizations of competence.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".