Development of a One-On-One Complementary Medicine (CAM) Decision Support Coaching Intervention for Cancer Patient and Families
Bibliographic record
Abstract
Background: Up to 80% of cancer patients use complementary medicine (CAM), yet most do not receive adequate decision support from health professionals to safely integrate CAM into their cancer treatment plan. This gap in care leads to concerns about safety when combining CAM with cancer treatments, and possible missed benefits from CAM therapies for which positive evidence exists. Purpose: This presentation outlines the development and pilot testing of a nurse-led intervention to address this gap in care. The one-on-one CAM decision support coaching intervention (CAM DSCI) offers cancer patients with complex CAM decision support needs (e.g. multiple CAM therapy use, high distress levels, considering conventional treatment delays) a structured approach to accessing and contextualizing evidence-informed CAM information to their unique clinical and personal situation. Methods: Using a convenience sample and mixed methods approach, the pilot study evaluated a) participants’ CAM decision support needs, b) how the CAM DSCI affects select patient outcomes, and c) CAM DSCI feasibility (time, resources, expertise). Findings: All participants (N=20) demonstrated improvements post CAM DSCI in CAM knowledge, decision quality, and decisional regret and described reduced anxiety and confusion when making CAM decisions. A range of CAM decision support needs were identified and feasibility of the intervention for the practice setting was established, including development of a practice-ready CAM assessment and decision support tool for health professionals. Implications: The pilot study offers preliminary support for feasibility and effectiveness of the CAM DSCI to meet complex patient CAM decision support needs. This intervention also highlights an innovative role for nurses in the growing field of CAM/Integrative Medicine.
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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 | 0.000 |
| 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".