Exploring Cancer Patients’ Experiences of an Online Mindfulness-Based Program: A Qualitative Investigation
Bibliographic record
Abstract
OBJECTIVE: Chronic neuropathic pain (CNP) is a common condition cancer survivors experience. Mindfulness training may be one approach to address the psychosocial factors associated with CNP. The purpose of this study was to understand patients' experiences in an 8-week online mindfulness-based program (MBP), including techniques and skills learned and applied, barriers to practice, and research experiences. METHODS: Nineteen participants who were part of a randomized controlled trial consented to participate in a telephone interview or submit written responses via email post-course. Interviews were transcribed and analyzed using the principles of Applied Thematic Analysis (ATA). RESULTS: Predominant themes identified in participant interviews included (1) common humanity, (2) convenience, (3) teacher resonance, (4) perceived relaxation and calm, (5) pain and stress management, (6) half-day session, and (7) mindful breathing. Participants also identified helpful strategies learned and implemented from the course, as well as barriers to practice, and key components of their experiences in a randomized controlled trial, including a sense of disconnection post-course and needing continued ongoing sessions, and the importance of the facilitators' skills in creating a comfortable and supportive space. CONCLUSIONS: An online group-based MBP may offer a more accessible resource and form of psychosocial intervention and support for cancer survivors living with CNP. Furthermore, the need and consideration for implementing ongoing group maintenance sessions to minimize participants' feelings of disconnect and abandonment post-course and post-study are warranted in future MBP development.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".