Laying the foundation for iCANmeditate: A mixed methods study protocol for understanding patient and oncologist perspectives on meditation
Bibliographic record
Abstract
BACKGROUND: People with cancer experience heightened levels of stress and anxiety, including psychological or physical. In recent years, digitally delivered complimentary therapies, such as meditation, have gained attention in cancer research and advocacy communities for improving quality of life. However, most digital meditation resources are commercially available and are not tailored to the unique needs of cancer patients (addressing fears of recurrence). As such, this study lays the foundation to co-design a publicly available digital meditation program called iCANmeditate that contains cancer-specific meditation content. AIMS: To understand: (1) cancer patients' perceptions and practices of meditation, as well as their needs in addressing the stress that accompanies their cancer diagnosis and (2) current knowledge of meditation and prescribing trends amongst oncologists in Canada. METHODS AND ANALYSIS: A mixed-methods design comprised of online patient and oncologist surveys and interviews with patients will be used. Survey data analysis will use multivariate logistic regressions to examine predictors of: (1) interest in using a meditation app among patients and (2) prescribing meditation among oncologists. Patient interviews will gather insights about the contexts of daily living where meditation would be most beneficial for people with cancer; this data will be analyzed thematically. DISCUSSION: The results of this study will inform iterative co-design workshops with cancer patients to build the digital meditation program iCANmeditate; interview results will be used to develop vignettes or "personas" that will supply the initial stimulus material for the iterative co-design workshops. Once the program has been finalized in partnership with cancer patient participants, a usability and pilot study will follow to test the functionality and efficacy of the tool. Results from the oncologist survey will form the basis of knowledge mobilization efforts to facilitate clinical buy-in and awareness of the benefits of meditation to cancer patients.
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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 | 0.072 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".