Listening in: Identifying Considerations for Integrating Complementary Therapy into Oncology Care Across Patient, Clinic, and System Levels—A Case Example of a Digital Meditation Tool
Bibliographic record
Abstract
Purpose: As cancer treatments and survival rates continue to improve, integrating supportive complementary therapies into oncology practice is increasingly important. Identifying patient- and clinic-level considerations can guide the selection and integration of evidence-based and effective therapies. Using the Behavioural Design Space (BDS), this study illustrates how a design framework can facilitate the identification of patient needs, clinic requirements, and system-level constraints prior to implementing a digital meditation tool (DMT). Methods: A cross-sectional survey of cancer patients in active treatment to assess distress, attitudes, barriers, and knowledge of meditation. Descriptive statistics and binary multivariate logistic regressions examined associations between patient characteristics and interest in meditation or using a DMT. Findings were mapped onto the six elements of the BDS framework in consultation with clinic staff. Results: Among 148 patients surveyed, 65% had never meditated, yet 42% expressed interest in using a DMT. Greater engagement in stress-coping activities was the strongest predictor of interest in both learning meditation and using a DMT. Female sex increased, while age decreased, the odds of interest in using a DMT. Conclusions: Integrating complementary therapies into oncology care requires attention to patient, clinic, and system-level factors. The BDS framework can guide the therapy/tool selection process by highlighting patient needs, potential barriers, and implementation challenges. Future work should focus on operationalizing the BDS into a practical decision-making tool for healthcare providers.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".