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Record W2149367461 · doi:10.1177/1043454206296018

Effects of Distraction on Pain, Fear, and Distress During Venous Port Access and Venipuncture in Children and Adolescents With Cancer

2006· article· en· W2149367461 on OpenAlexaff
Andrea Windich-Biermeier, Isabelle Sjoberg, Juanita Conkin Dale, Debra A. Eshelman, Cathie E. Guzzetta

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2006
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsVenipunctureDistractionDistressMedicinePort (circuit theory)Venous accessCancerAnesthesiaPhysical therapyPsychologyClinical psychologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

This study evaluates the effect of self-selected distracters (ie, bubbles, I Spy: Super Challenger book, music table, virtual reality glasses, or handheld video games) on pain, fear, and distress in 50 children and adolescents with cancer, ages 5 to 18, with port access or venipuncture. Using an intervention-comparison group design, participants were randomized to the comparison group (n = 28) to receive standard care or intervention group (n = 22) to receive distraction plus standard care. All participants rated their pain and fear, parents rated participant fear, and the nurse rated participant fear and distress at 3 points in time: before, during, and after port access or venipuncture. Results show that self-reported pain and fear were significantly correlated (P = .01) within treatment groups but not significantly different between groups. Intervention participants demonstrated significantly less fear (P <.001) and distress (P = .03) as rated by the nurse and approached significantly less fear (P = .07) as rated by the parent. All intervention parents said the needlestick was better because of the distracter. The authors conclude that distraction has the potential to reduce fear and distress during port access and venipuncture.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.289
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations245
Published2006
Admission routes1
Has abstractyes

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