Being Mindful of Our Ps and Qs: A Systematic-Narrative Review of the Parallels and Quality of Digital Mindfulness-Based Programs for Cancer Patients
Bibliographic record
Abstract
This narrative review aimed to evaluate the quality and characteristics of digitally delivered mindfulness-based programs (MBP) for cancer patients. It sought to gain a deeper understanding of the similarities and differences in the delivery, components, and success of existing interventions by evaluating the adherence of the program’s components to MBP principles. In total, six databases (CINAHL, EMBASE, Medline, Web of Science, PubMed, and PsycINFO) were searched from inception to March 2024, and identified papers’ references were reviewed. Two reviewers independently determined article inclusion (original studies, English-language, digital delivery of MBP for adult cancer samples), evaluated adherence to mindfulness principles, and extracted data using a standardized template. Interventions were evaluated for adherence to essential components of MBPs as set out by Crane and colleagues (i.e., program characteristics and facilitator qualifications). Overall, 35 published papers reporting on the efficacy of 30 unique interventions were reviewed. Ten interventions adhered to MBP principles, while 20 did not. Sample sizes varied across studies. Interventions demonstrated significant differences in their structure (e.g., length, delivery method, teacher-led/self-guided), efficacy-testing methodology (e.g., randomized controlled trial vs. one-armed trial), and intervention description lengths. However, all interventions demonstrated the ability to significantly change at least one mental health-related patient outcome. Future evaluation studies of MBPs must take greater caution when labeling interventions as mindfulness-based , and greater transparency is required for reporting on intervention content, fidelity to established protocols and MBP principles, and facilitator qualifications. Lastly, intervention recommendations made to cancer patients should consider patient preferences and limitations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".