The essential enterprise: the critical role of accreditation in the 21st century
Bibliographic record
Abstract
Health professions education (HPE) is undergoing rapid change to a competency-based world, and accreditation change is part of that story [1][2][3][4].Despite over 100 years of scientific, instructional, and biomedical innovation, health professions education continues to face criticism.Deficits and variations in graduate competence, patient harm, and preparedness for modern health care are considered major challenges for current designs for HPE [5,6].Can accreditation help to address these issues?Accreditation is commonly viewed as an essential component of an effective health professions education (HPE) system, valued both as a lever for quality assurance as well as for continuous quality improvement.However, for such an essential enterprise, the body of literature on HPE accreditation is small.Accreditation systems exist worldwide in a wide variety of forms.Do we all agree on what we mean by "accreditation"?What are the essential components of an accreditation system?What works best for a given context?What are the emerging issues in contemporary education?What are best and "next" practices?We have only the work of a few pioneering scholars to inform these questions, and no global consensus on which to advance our thinking.Enter an accreditation community of practice, the International Health Professions Accreditation Outcomes Consortium (IHPAOC).We founded this organization in 2012 to advance the practice of HPE accreditation.To date, this group has organized two world summits on HPE accreditation, one in 2013 in conjunction with
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.030 | 0.030 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".