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Record W2131607564 · doi:10.1093/jpepsy/jsu029

Systematic Review and Meta-Analysis of Distraction and Hypnosis for Needle-Related Pain and Distress in Children and Adolescents

2014· review· en· W2131607564 on OpenAlexafffund
Kathryn A. Birnie, Mélanie Noël, Jennifer A. Parker, Christine T. Chambers, Lindsay S Uman, Steve Kisely, Patrick J. McGrath

Bibliographic record

VenueJournal of Pediatric Psychology · 2014
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchIWK Health Centre
KeywordsDistractionHypnosisDistressPsychological interventionIntervention (counseling)Randomized controlled trialMedicineClinical psychologyMeta-analysisPsychologyPhysical therapyPsychiatrySurgeryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To systematically review the evidence (and quality) for distraction and hypnosis for needle-related pain and distress in children and adolescents. To explore the effects of distraction characteristics (e.g., adult involvement, type of distracter), child age, and study risk of bias on treatment efficacy. METHODS: 26 distraction and 7 hypnosis trials were included and self-report, observer-report, and behavioral pain intensity and distress examined. Distraction studies were coded for 4 intervention characteristics, and all studies coded for child age and study risk of bias. RESULTS: Findings showed strong support for distraction and hypnosis for reducing pain and distress from needle procedures. The quality of available evidence was low, however. Characteristics of distraction interventions, child age, and study risk of bias showed some influence on treatment efficacy. CONCLUSIONS: Distraction and hypnosis are efficacious in reducing needle-related pain and distress in children. The quality of trials in this area needs to be improved.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.046
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.017
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.382
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations289
Published2014
Admission routes2
Has abstractyes

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