The ONE‐MIND Study: Rationale and protocol for assessing the effects of ONlinE MINDfulness‐based cancer recovery for the prevention of fatigue and other common side effects during chemotherapy
Bibliographic record
Abstract
Cancer patients often experience poor quality of life (QoL) during chemotherapy (CT) treatments due to side effects including fatigue, insomnia, pain and nausea/vomiting. Mindfulness-based cancer recovery (MBCR) is an evidence-based intervention for treating such symptoms, but has not been investigated as an adjunctive treatment during CT. This study aims to determine the efficacy of an online group MBCR programme delivered during CT in 12 real-time interactive weekly sessions for managing fatigue (primary outcome). Secondary outcomes include sleep disturbance, pain, nausea/vomiting, mood, stress and QoL. Exploratory outcomes include cognitive function, white blood cell counts and return to work. The study is a two-armed randomised controlled waitlist trial with 2:1 allocation to treatment (online group MBCR during CT) or control (waitlist usual care; online MBCR following CT completion) with a target sample size of N = 178. Participants are breast or colorectal cancer patients undergoing common CT regimens in Calgary, Canada. Online assessments using validated self-reported instruments will take place at baseline, post-MBCR, post-CT and 12 months' post-baseline. If online MBCR delivered during CT significantly reduces fatigue in cancer patients' post-CT and also impacts secondary symptoms, this would provide evidence for including mindfulness training as an adjunctive symptom management therapy during CT.
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".