Ten ways to get a grip on designing and implementing a competency-based medical education training program
Bibliographic record
Abstract
BACKGROUND: Globally there is a move to adopt competency-based medical education (CBME) at all levels of the medical training system. Implementation of a complex intervention such as CBME represents a marked paradigm shift involving multiple stakeholders. METHODS: This article aims to share tips, based on review of the available literature and the authors' experiences, that may help educators implementing CBME to more easily navigate this major undertaking and avoid "black ice" pitfalls that educators may encounter. RESULTS: Careful planning prior to, during and post implementation will help programs transition successfully to CBME. Involvement of key stakeholders, such as trainees, teaching faculty, residency training committee members, and the program administrator, prior to and throughout implementation of CBME is critical. Careful and selective choice of key design elements including Entrustable Professional Activities, assessments and appropriate use of direct observation will enhance successful uptake of CBME. Pilot testing may help engage faculty and learners and identify logistical issues that may hinder implementation. Academic advisors, use of curriculum maps, and identifying and leveraging local resources may help facilitate implementation. Planned evaluation of CBME is important to ensure choices made during the design and implementation of CBME result in the desired outcomes. CONCLUSION: Although the transition to CBME is challenging, successful implementation can be facilitated by careful design and strategic planning.
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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 | 0.231 | 0.147 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.019 | 0.055 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.013 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".