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Record W2440318886 · doi:10.1093/jpepsy/jsw045

A Multi-Informant Multi-Method Investigation of Family Functioning and Parent–Child Coping During Children’s Acute Pain

2016· article· en· W2440318886 on OpenAlexafffund
Kathryn A. Birnie, Christine T. Chambers, Jill Chorney, Conrad V. Fernandez, Patrick J. McGrath

Bibliographic record

VenueJournal of Pediatric Psychology · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersCanadian Institutes of Health Research
KeywordsCoping (psychology)DistressPsychologyPain catastrophizingClinical psychologyDevelopmental psychologyChronic painPsychiatry

Abstract

fetched live from OpenAlex

Objective: To explore relations between family functioning and child acute pain, including pain ratings, coping, and parent–child behaviors. Methods: Community sample of 171 dyads including one child aged 8–12 years (52% girls) and one parent (79% mothers). Family functioning was assessed via child and parent self-report, and observation during a conflict discussion task. Children and parents rated pain catastrophizing at baseline, and child pain and distress following a cold pressor task (CPT). Parent–child interactions during the CPT were coded for observed behaviors during child pain. Results: Self-report of poorer family functioning predicted greater child and parent pain catastrophizing, and parent distress. Less observed family negativity/conflict and cohesiveness, and greater family focus of problems and parent emotional support predicted more child symptom complaints. Family functioning was not associated with child pain or distress. Conclusions: Family functioning influenced parent and child coping and child behavioral responses, but not the experience, of acute pain.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.339
Teacher spread0.307 · 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 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

Citations15
Published2016
Admission routes2
Has abstractyes

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