Complementary and integrative medicine best practice guidelines: changing oncology health care providers' knowledge, attitudes and practices
Bibliographic record
Abstract
Complementary and integrative medicine (CIM) use is prevalent among cancer patients and oncology health care providers (HCP) need to be knowledgeable and address CIM use to provide safe, patient-centred care. This study assessed how the implementation of a CIM best practice guideline through an educational intervention and a CIM assessment form affected the knowledge, readiness, attitudes, and practices of 31 oncology HCP at a Canadian cancer centre. Using a before-after study design, participants’ self-reported knowledge, readiness, attitudes, and practices around CIM were assessed prior to the intervention and again three months later. After completing the education intervention and implementing the CIM assessment form over the 3-month time period, participants reported a significant improvement in CIM knowledge, readiness to support cancer patients’ CIM decisions, and willingness to consult with another HCP about CIM. However, participants’ attitudes towards CIM, and clinical practices such as asking about CIM use and providing CIM decision support did not significantly change. These findings highlight the importance of health professional education related to CIM in cancer care setting and the value of a CIM assessment tool to strengthen oncology HCPs’ knowledge about CIM, and increase their readiness to address cancer patients’ CIM use. Such standardized training also holds the potential to shift oncology HCPs’ clinical practice related to CIM and provide more comprehensive and safer patient care.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".