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Record W3191743677 · doi:10.1093/jpepsy/jsab074

Focusing on Young Children in Pediatric Psychology Research: Introduction to the Special Issue on Young Children

2021· article· en· W3191743677 on OpenAlexaff
Carrie Tully, C. Meghan McMurtry, Randi Streisand

Bibliographic record

VenueJournal of Pediatric Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of GuelphMcMaster Children's HospitalWestern University
Fundersnot available
KeywordsPediatric psychologyIntervention (counseling)Observational studyPsychologyDevelopmental psychologyClinical psychologyMedicinePediatricsPsychiatry

Abstract

fetched live from OpenAlex

Abstract This special issue focusing on pediatric psychology research in young children highlights 15 papers that focus on infants through preschoolers. Studies selected for inclusion cover a range of pediatric health conditions such as food allergy, medical trauma, injuries/traumatic brain injury, cancer, inflammatory bowel disease, pain, and sleep. The inherent challenges of researching young children are described, and studies vary in their methods for assessment and intervention; multiple studies include an observational component or developmental evaluation. Six of the studies employ diverse samples of children and/or parents and demonstrate the feasibility as well as importance of increasing our understanding of factors related to health disparities. Taken together, the special issue demonstrates the high quality of research focusing on young children. As the special issue editors, we hope this collection will spark an interest in others to focus on research with young children across pediatric populations.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.450
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.037
GPT teacher head0.372
Teacher spread0.336 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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