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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".