Response to a Proposal for an Integrative Medicine Curriculum
Bibliographic record
Abstract
BACKGROUND: A paper entitled "Core Competencies in Integrative Medicine for Medical School Curricula: A proposal," published in Academic Medicine, stimulated a broad discussion among complementary and alternative medicine (CAM) educators. This discussion led to a formal process for responding to the issues raised by the paper. METHODS: Representatives from the Academic Consortium for Complementary and Alternative Health Care (ACCAHC) and the Oregon Collaborative for Complementary and Integrative Medicine (OCCIM) formed the ACCAHC/OCCIM Task Force to participate in a Delphi process of consultation and deliberation. This process led to a broad, cross-discipline agreement on important points to include in a response to the integrative medicine (IM) curriculum proposal. RESULTS: Five key areas of concern emerged: (1) the definition of IM as presented in the paper; (2) lack of clarity about the goals of the proposed IM curriculum; (3) lack of recognition of the breadth of whole systems of health care; (4) omission of competencies related to collaboration between MDs and CAM professionals in patient care; and (5) omission of potential areas of partnership in IM education. CONCLUSIONS: A major overall theme emerging from the Delphi process was a desire for closer collaboration between conventional medical schools and CAM academic institutions in developing IM curricula. Several cross-disciplinary venues for addressing the Delphi Task Force themes include the National Center for Complementary and Alternative Medicine's R-25 Initiatives, and the National Education Dialogue. OCCIM is presented as an example of a successful lateral integration approach.
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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 | 0.025 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".