Advancing and sustaining excellence in EPA-based curricula
Bibliographic record
Abstract
The quality of health professions education is socially determined and closely linked to the quality of health care. Entrustable professional activities (EPAs) add strength to and operationalize curricula for competency-based education for health professions by focusing on both the patient and trainee, bringing health professions education together with patient care. This social accountability within an EPA-based curriculum emphasizes measurable enhancements to local health services through EPAs. As such, both external quality assurance (QA) and internal QA are crucial for implementing and improving an EPA-based program. External QA involves guidance from the regulating body regarding training policies, procedures, and practices. Internal QA entails self-auditing, utilizing mechanisms like program evaluation (PE) to monitor, evaluate, and improve the assessment and attainment of EPAs. Continuous quality improvement (CQI) can be used to augment PE by serving as a system for accountability and transparency. This section introduces the concepts of PE and CQI to be used within an EPA-based curriculum, models to support PE and CQI processes, examples of actual cases where PE and CQI were beneficial, and solutions to address challenges specific to EPA-based curricula.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".