Medical School Use of Competency Frameworks in Learning Objectives and Curriculum
Bibliographic record
Abstract
Medical schools use diverse language in their learning objectives, including those at the program, course, and event level. Schools may choose to write original learning objectives, use an existing competency frameworks’ language exactly as is, or customized an existing competency frameworks’ language to best fit their school. This study examines the question – which competency frameworks do US allopathic medical schools in their learning objectives, how do they use them, and how does this play out in curriculum? Four frameworks are examined: the Association of American Medical Colleges (AAMC) Physician Competency Reference Set (PCRS), the Accreditation Council on Graduate Medical Education (ACMGE) core competencies, the AAMC Entrustrable Professional Activities (EPAs), and the Royal College of Physicians and Surgeons of Canada CanMeds enabling competencies. When examining Liaison Committee on Medical Education (LCME) accredited, United States-based allopathic medical schools’ curricular data from 2014-2020, results show that a majority of schools (82 unique schools) use one or more of the four frameworks in the language of their program objectives. There is an upward trend over time of schools incorporating language from frameworks into their learning objectives. When examining unique school uses, the most often chosen of the four frameworks is the AAMC PCRS at the program, course, and event objective level. When examining whether schools use a framework’s language exactly as is, or adapt it to add additional characters, the more common approach is to adapt the frameworks’ language for school-specific learning objectives.
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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".