Mindfulness‐based arts interventions for cancer care: A systematic review of the effects on wellbeing and fatigue
Bibliographic record
Abstract
OBJECTIVE: Upon receiving a cancer diagnosis, life irrevocably changes and complex experiences of emotional distress often occur. There is a growing interest in mindfulness-based arts interventions (MBAIs) to ameliorate the distress many patients experience. Our review objective was to synthesize the evidence on the effectiveness of MBAIs on psychological wellbeing and fatigue. METHOD: Relevant quantitative articles were identified through a systematic search of the grey literature and online databases including MEDLINE, CINAHL, Cochrane CENTRAL, Art Full Text, ART bibliographies Modern, PsycINFO, Scopus, and EMBASE. Two independent reviewers screened titles/abstracts against predetermined inclusion criteria, read full-text articles for eligibility, conducted quality appraisals of included articles, and extracted pertinent data with a standardized data extraction form. The heterogeneity of the included studies precluded a meta-analysis and a narrative synthesis of study outcomes was conducted. RESULTS: Our systematic search retrieved 4241 titles/abstracts, and 13 studies met our inclusion criteria (eight randomized controlled trials and five quasi-experiments). Most of the studies focused on patients with cancer (92.3%). There is a growing interest in MBAIs over time and significant heterogeneity in the types of interventions. A significant effect was found on several outcomes that are important in psychosocial oncology: quality of life, psychological state, spiritual wellbeing, and mindfulness. The effect on fatigue was equivocal. CONCLUSIONS: This novel intervention demonstrates promise for the psychosocial care of patients with cancer. These findings are an essential antecedent to the continued implementation, development, and evaluation of MBAIs in oncology.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".