Effective accreditation in postgraduate medical education: from process to outcomes and back
Bibliographic record
Abstract
BACKGROUND: The accreditation of medical educational programs is thought to be important in supporting program improvement, ensuring the quality of the education, and promoting diversity, equity, and population health. It has long been recognized that accreditation systems will need to shift their focus from processes to outcomes, particularly those related to the end goals of medical education: the creation of broadly competent, confident professionals and the improvement of health for individuals and populations. An international group of experts in accreditation convened in 2013 to discuss this shift. MAIN TEXT: Participants unequivocally supported the inclusion of more outcomes-based criteria in medical education accreditation, specifically those related to the societal accountability of the institutions in which the education occurs. Meaningful and feasible outcome metrics, however, are hard to identify. They are regionally variable, often temporally remote from the educational program, difficult to measure, and susceptible to confounding factors. The group identified the importance of health outcomes of the clinical milieu in which education takes place in influencing outcomes of its graduates. The ability to link clinical data with individual practice over time is becoming feasible with large repositories of assessment data linked to patient outcomes. This was seen as a key opportunity to provide more continuous oversight and monitoring of program impact. The discussants identified several risks that might arise should outcomes measures completely replace process issues. Some outcomes can be measured only by proxy process elements, and some learner experience issues may best be measured by such process elements: in brief, the "how" still matters. CONCLUSIONS: Accrediting bodies are beginning to view the use of practice outcome measures as an important step toward better continuous educational quality improvement. The use of outcomes will present challenges in data collection, aggregation, and interpretation. Large datasets that capture clinical outcomes, experience of care, and health system performance may enable the assessment of multiple dimensions of program quality, assure the public that the social contract is being upheld, and allow identification of exemplary programs such that all may improve. There remains a need to retain some focus on process, particularly those related to the learner experience.
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 | 0.265 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".