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Randomized clinical trial of musical distraction with and without headphones for adolescents’ immunization pain

2010· article· en· W2148404010 on OpenAlexaff
Ólöf Kristjánsdóttir, Guðrún Kristjánsdóttir

Bibliographic record

VenueScandinavian Journal of Caring Sciences · 2010
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsDistractionHeadphonesRandomized controlled trialMusicalMedicineAudiologyImmunizationPhysical therapyPsychologyPhysical medicine and rehabilitationCognitive psychologySurgeryArtVisual arts

Abstract

fetched live from OpenAlex

Distraction has shown to be a helpful pain intervention for children; however, few investigations have studied the effectiveness of this method with adolescents. The aim of this study was to evaluate the usefulness of an easy and practical musical distraction in reducing adolescents' immunization pain. Furthermore, to examine whether musical distraction techniques (with or without headphones) used influenced the pain outcome. Hundred and eighteen 14-year-old adolescents, scheduled for polio immunization, participated. Adolescents were randomly assigned to one of three research groups; musical distraction with headphones (n=38), musical distraction without headphones (n=41) and standard care control (n=39). Results showed adolescents receiving musical distraction were less likely to report pain compared to the control group, controlling for covariates. Comparing musical distraction techniques, eliminating headphone emerged as a significant predictor of no pain. Results suggest that an easy and practical musical distraction intervention, implemented without headphones, can give some pain relief to adolescents during routine vaccination.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.035
GPT teacher head0.364
Teacher spread0.329 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations55
Published2010
Admission routes1
Has abstractyes

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