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Record W1972694345 · doi:10.3109/17518423.2010.499409

Community integration interventions for youth with acquired brain injuries: A review

2010· review· en· W1972694345 on OpenAlexaff
Sabrina Agnihotri, Michelle Keightley, Angela Colantonio, Debra Cameron, Helene J. Polatajko

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2010
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsPublic Health OntarioToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsCommunity integrationPsychological interventionAcquired brain injuryPsychologyPhysical medicine and rehabilitationTraumatic brain injuryMedicinePhysical therapyRehabilitationPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify and summarize published literature that examined the effectiveness of social and community integration interventions for children and adolescents with ABI in order to provide recommendations regarding future research on this topic. METHODS: A literature review was conducted to identify studies that focused on social and community integration interventions for youth with ABI. Further manual searching of relevant journals with a paediatric rehabilitation focus was also carried out. RESULTS: Currently, limited research has been published evaluating such interventions. The lack of research may stem largely from issues relating to how to measure community integration. Recommendations regarding intervention settings and structure are discussed. CONCLUSION: Additional studies investigating social and community integration interventions are necessary, including those with measures tailored specifically to community integration, larger samples, as are better controls and recruitment of youth with varying severities of brain injuries.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.253
GPT teacher head0.458
Teacher spread0.205 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations21
Published2010
Admission routes1
Has abstractyes

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