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Record W3044196470 · doi:10.1177/1043454220944126

Mindfulness-Based Interventions for Symptom Management in Children and Adolescents With Cancer: A Systematic Review

2020· review· en· W3044196470 on OpenAlexaff
Deborah Tomlinson, Lillian Sung, Emily Vettese, Shelley Murphy, Erin Plenert

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2020
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of TorontoInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsPsychological interventionCINAHLPsycINFOMedicineMEDLINEMindfulnessDistressQuality of life (healthcare)Intervention (counseling)Systematic reviewClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.426
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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