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Record W2035066883 · doi:10.1080/09638280801945899

Social adjustment at school: Are children with cerebral palsy perceived more negatively by their peers than other at-risk children?

2008· article· en· W2035066883 on OpenAlexafffund
Line Nadeau, Réjean Tessier

Bibliographic record

VenueDisability and Rehabilitation · 2008
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersInstitut de Réadaptation en Déficience Physique de Québec
KeywordsCerebral palsyPsychologyDevelopmental psychologyTypically developingMedicinePhysical medicine and rehabilitationPhysical therapyAutism

Abstract

fetched live from OpenAlex

PURPOSE: To compare three dimensions of social adjustment (social status, friendship and victimization) across four groups of children between the ages of nine and 12 who differ by their birth status (premature vs. at term) and the presence or absence of a motor impairment (with and without cerebral palsy [CP]). METHOD: All premature (n = 72) and term children (n = 118) without CP and all children with CP (premature with CP: n = 49; term with CP: n = 29) are part of a follow-up study. Social adjustment measures were obtained by conducting a classwide sociometric interview in the class of the target child. RESULTS: Irrespective of their birth status, girls with CP have more social adjustment problems than those without a disability. With respect to victimization, the results show that, irrespective of gender, both CP children and premature children (without CP) differ from their term peers (without CP). CONCLUSIONS: By comparing the four groups, we are able to qualify the impact of a visible clinical impairment such as CP versus that of extreme prematurity on social adjustment.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.245
Teacher spread0.234 · 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

Citations26
Published2008
Admission routes2
Has abstractyes

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