Education Competencies for Integrative Oncology—Results of a Systematic Review and an International and Interprofessional Consensus Procedure
Bibliographic record
Abstract
Integrative oncology is a burgeoning field and typically provided by a multiprofessional team. To ensure cancer patients receive effective, appropriate, and safe care, health professionals providing integrative cancer care should have a certain set of competencies. The aim of this project was to define core competencies for different health professions involved in integrative oncology. The project consisted of two phases. A systematic literature review on published competencies was performed, and the results informed an international and interprofessional consensus procedure. The second phase consisted of three rounds of consensus procedure and included 28 experts representing 7 different professions (medical doctors, psychologists, nurses, naturopathic doctors, traditional Chinese medicine practitioners, yoga practitioners, patient navigators) as well as patient advocates, public health experts, and members of the Society for Integrative Oncology. A total of 40 integrative medicine competencies were identified in the literature review. These were further complemented by 18 core oncology competencies. The final round of the consensus procedure yielded 37 core competencies in the following categories: knowledge (n = 11), skills (n = 17), and abilities (n = 9). There was an agreement that these competencies are relevant for all participating professions. The integrative oncology core competencies combine both fundamental oncology knowledge and integrative medicine competencies that are necessary to provide effective and safe integrative oncology care for cancer patients. They can be used as a starting point for developing profession-specific learning objectives and to establish integrative oncology education and training programs to meet the needs of cancer patients and health professionals.
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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 | 0.181 | 0.304 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.052 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".