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Record W2105091892 · doi:10.1093/jpepsy/jsu162

Automated Parent-Training for Preschooler Immunization Pain Relief: A Randomized Controlled Trial

2015· article· en· W2105091892 on OpenAlexaff
L. L. Cohen, Nikita Rodrigues, C. S. Lim, Donald J. Bearden, Josie S. Welkom, Naomi E. Joffe, Patrick J. McGrath, Laura A. Cousins

Bibliographic record

VenueJournal of Pediatric Psychology · 2015
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsCapital District Health Authority
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsDistractionCoachingDistressRandomized controlled trialParent trainingPsychologyDevelopmental psychologyClinical psychologyMedicinePsychiatryPsychotherapistIntervention (counseling)

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine a computerized parent training program, "Bear Essentials," to improve parents' knowledge and coaching to help relieve preschoolers' immunization distress. METHOD: In a randomized controlled trial, 90 parent-child dyads received Bear Essentials parent training plus distraction, distraction only, or control. Outcomes were parent knowledge, parent and child behavior, and child pain. RESULTS: Bear Essentials resulted in improved knowledge of the effects of parents' reassurance, provision of information, and apologizing on children's procedural distress. Trained parents also engaged in less reassurance and more distraction and encouragement of deep breathing. Children in Bear Essentials engaged in more distraction and deep breathing than children in other groups. There were no effects on measures of child distress or pain. CONCLUSIONS: Results suggest that the interactive computer training program impacted parent knowledge, parent behavior, and child behavior as hypothesized, but modifications will be necessary to have more robust outcomes on child procedural distress.

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)0.019
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.056
GPT teacher head0.379
Teacher spread0.322 · 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 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

Citations24
Published2015
Admission routes1
Has abstractyes

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