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Record W2313788060 · doi:10.1177/1359104513487001

Use of narratives to assess language disorders in an inpatient pediatric psychiatric population

2013· article· en· W2313788060 on OpenAlexaff
Patsy Steig Pearce, Carolyn E. Johnson, Patricia C. Manly, Jake Locke

Bibliographic record

VenueClinical Child Psychology and Psychiatry · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British ColumbiaBC Children's Hospital
Fundersnot available
KeywordsNarrativePsychologyComprehensionDistressCognitionDevelopmental psychologyPopulationWorking memoryCognitive psychologyClinical psychologyPsychiatryMedicineLinguistics

Abstract

fetched live from OpenAlex

A large proportion of child psychiatry patients have undiagnosed language disorders. Adequately developed language is critical for psychotherapy and cognitive-behavioral therapies. This study investigated (1) whether assessment of oral narratives would identify language impairments in this population undetected by assessment of only core language abilities, and (2) the extent to which measures of cognition, working memory, emotional distress, and social function differentially predict core language and narrative development. Results showed that (1) more than twice as many children were identified with language impairment when both narrative and core language assessment were used, and (2) core language comprehension and complex verbal working memory were the strongest predictors of narrative production, while core language comprehension, a less complex working-memory task, and social skills best predicted narrative comprehension. Emotional distress did not predict either. The results emphasize the importance of evaluating child psychiatry patients' language, using both core language and narrative measures.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.404
Teacher spread0.355 · 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 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

Citations5
Published2013
Admission routes1
Has abstractyes

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