Mindfulness-Based Interventions for Symptom Management in Children and Adolescents With Cancer: A Systematic Review
Bibliographic record
Abstract
Psychological interventions have shown benefit in reducing symptoms in children and adolescents with cancer. More recently, mindfulness-based interventions (MBIs) have been shown to be a promising approach to symptom intervention in adolescents with chronic illnesses. In this systematic review, we aimed to describe MBIs or focused-breathing interventions that have been used to treat symptoms in children receiving cancer therapy. A systematic review was conducted using MEDLINE/PubMed, EMBASE, CINAHL, and PsycINFO from inception to September 2019. We identified relevant articles in which MBIs or focused-breathing interventions were the primary interventions delivered to improve symptoms in children or adolescents with cancer. Six studies met the inclusion criteria. MBIs included controlled breathing and belly breathing. Intervention effects were found to be beneficial with regard to symptoms that included procedural pain, distress, and quality of life. The interventions were generally well accepted and beneficial. All studies suffered limitations because of methodological flaws, including the lack of randomization, and small sample sizes. Despite the small numbers of studies and participants, MBIs delivered to children with cancer may have beneficial effects on certain symptoms. Implications for future research include interventions tailored to the specific symptom burden. Studies must aim to increase sample sizes as well as to include individuals at high risk for severe 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".