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Strategies for improving disability awareness and social inclusion of children and young people with cerebral palsy

2011· article· en· W2150132939 on OpenAlexafffund
Sally Lindsay, Amy C. McPherson

Bibliographic record

VenueChild Care Health and Development · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalPublic Health OntarioUniversity of Toronto
FundersMinistère de l’Éducation, Gouvernement de l’Ontario
KeywordsCerebral palsyInclusion (mineral)PsychologyContext (archaeology)Developmental psychologyFocus groupTypically developingClinical psychologyPsychiatryAutismSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Children and youth with disabilities are at a higher risk of being socially excluded or bullied while at school compared with their typically developing peers. This study explored disabled children's suggestions for improving social inclusion. METHODS: Fifteen children with cerebral palsy were interviewed or took part in a group discussion about social inclusion and bullying. All interviews and focus groups were audio-recorded and transcribed verbatim. RESULTS: The children and youth described several strategies to help improve social inclusion at school including: (1) disclosure of condition and creating awareness of disability; (2) awareness of bullying; (3) developing a peer support network and building self-confidence; and (4) suggestions on what teachers can do. CONCLUSIONS: It is recommended that children's suggestions be considered within the classroom context to enhance the social inclusion and participation of children with disabilities.

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)0.005
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.266
Teacher spread0.250 · 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

Citations75
Published2011
Admission routes2
Has abstractyes

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