Overarching challenges to the implementation of competency-based medical education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical education is under increasing pressure to more effectively prepare physicians to meet the needs of patients and populations. With its emphasis on individual, programmatic, and institutional outcomes, competency-based medical education (CBME) has the potential to realign medical education with this societal expectation. Implementing CBME, however, comes with significant challenges. This manuscript describes four overarching challenges that must be confronted by medical educators worldwide in the implementation of CBME: (1) the need to align all regulatory stakeholders in order to facilitate the optimization of training programs and learning environments so that they support competency-based progression; (2) the purposeful integration of efforts to redesign both medical education and the delivery of clinical care; (3) the need to establish expected outcomes for individuals, programs, training institutions, and health care systems so that performance can be measured; and (4) the need to establish a culture of mutual accountability for the achievement of these defined outcomes. In overcoming these challenges, medical educators, leaders, and policy-makers will need to seek collaborative approaches to common problems and to learn from innovators who have already successfully made the transition to CBME.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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 it