Exploring the Use of Neurofeedback by Cancer Survivors: Results of Interviews with Neurofeedback Providers and Clients
Bibliographic record
Abstract
OBJECTIVE: Cancer survivors may experience persistent physical and psychological symptoms following completion of cancer treatment. Neurofeedback is a noninvasive form of brain training reported to help with symptoms including pain, fatigue, depression, anxiety, insomnia, and cognitive decline; however, there is a lack of research exploring its use with cancer survivors. The objective of this study was to describe the experiences of neurofeedback and its impact on the lives of posttreatment cancer survivors as perceived by neurofeedback providers and cancer survivor clients. METHODS: This qualitative descriptive study employed semi-structured interviews and thematic analysis of interview transcripts. A convenience sample of twelve neurofeedback providers and five cancer survivor clients participated in this study. RESULTS: Thematic analysis revealed seven overarching themes as follows: (1) paying it forward; (2) transforming lives; (3) regaining control; (4) brain healing itself; (5) comforting experience, (6) accessibility, and (7) failure to respond. The first five themes related to benefits of neurofeedback, and the final two related to challenges of using neurofeedback with cancer survivors. CONCLUSIONS: Results support the use of neurofeedback to improve quality of life for cancer survivors; however, more research is needed to determine which neurofeedback systems and protocols are most effective for this population with persistent symptoms.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".