Integrating complementary and alternative medicine into cancer care: Canadian oncology nurses′ perspectives
Bibliographic record
Abstract
The integration of complementary and alternative medicine (CAM) and conventional cancer care in Canada is in its nascent stages. While most patients use CAM during their cancer experience, the majority does not receive adequate support from their oncology health care professionals (HCPs) to integrate CAM safely and effectively into their treatment and care. A variety of factors influence this lack of integration in Canada, such as health care professional(HCP) education and attitudes about CAM; variable licensure, credentialing of CAM practitioners, and reimbursement issues across the country; an emerging CAM evidence base; and models of cancer care that privilege diseased-focused care at the expense of whole person care. Oncology nurses are optimally aligned to be leaders in the integration of CAM into cancer care in Canada. Beyond the respect afforded to oncology nurses by patients and family members that support them in broaching the topic of CAM, policies, and position statements exist that allow oncology nurses to include CAM as part of their scope. Oncology nurses have also taken on leadership roles in clinical innovation, research, education, and advocacy that are integral to the safe and informed integration of evidence-based CAM therapies into cancer care settings in Canada.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".