Complementary and Alternative Medicine Online Learning Intervention for Oncology Healthcare Providers: A Mixed-Methods Study
Bibliographic record
Abstract
INTRODUCTION: With the increased usage of complementary approaches in oncology comes the need for its integration into healthcare professional (HCP) education. The purpose of this single-arm, mixed-methods study was to examine the feasibility and benefits of a brief complementary and alternative medicine (CAM) learning intervention for improving HCP knowledge, attitudes, and practices regarding CAM use in cancer care, and explore the experiences of participating HCPs. METHODS: HCPs from the Tom Baker Cancer Centre in Alberta, Canada, were invited to participate in 3 online interactive learning modules that reviewed: (1) basic CAM information, (2) HCP-patient CAM communication, and (3) evidence-based CAM decision support. The study survey consisted of attitude (n = 14), knowledge (n = 31), and practice (n = 31) items, administered at baseline and two-months post-intervention. Semi-structured interviews were conducted with a subset of participants. RESULTS: Approximately 300 HCPs were invited to participate, of which 105 expressed interest in the study (35%), and 83 of them consented to participate (79%). The intervention completion rate was 73% (61/83 HCPs). There was a significant pre-post change in HCPs' attitudes and, to a lesser extent, knowledge and practices related to CAM (8/14 attitude items changed pre-post compared to 13/31 knowledge items and 5/31 practice items), in which more HCPs reported patients should be assisted in making complementary therapy (CT) decisions, exhibited greater knowledge about CAM, and more often engaged in a CAM-related clinical practice. Qualitative findings supported the beneficial effects of the modules, with HCPs describing themselves as being more likely to ask patients about their CAM use and referring them to credible CAM resources. Nonetheless, the majority did not feel adequately prepared to make recommendations about specific CTs, even after the intervention. CONCLUSION: The current study suggests that online CAM learning offers a feasible and potentially promising intervention for improving oncology HCP knowledge, attitudes, and practices regarding CAM, warranting further investigation. This study highlights a need for institutional resources to help HCPs fully integrate CT decision support into cancer patient care. A coordinated evidence-based CAM program at cancer centers may help ensure that all patients' CAM-related needs are properly attended to.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".