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Record W1983599993 · doi:10.3109/02699052.2011.635353

Differences in social participation between individuals who do and do not attend brain injury drop-in centres: A preliminary study

2011· article· en· W1983599993 on OpenAlexaff
Alison McLean, Tal Jarus, Anita M. Hubley, Lyn Jongbloed

Bibliographic record

VenueBrain Injury · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British ColumbiaGF Strong Rehabilitation Centre
Fundersnot available
KeywordsAttendancePsychologyDrop outTraumatic brain injuryCommunity integrationSocial supportClinical psychologyPhysical therapyMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare social participation for individuals with traumatic brain injury (TBI) ≥1 year post-injury who attend brain injury drop-in centres (BIDCs) with individuals who do not attend but were identified as potentially benefitting from attending. RESEARCH DESIGN: Cross-sectional study with 23 individuals attending BIDCs and a comparison group of 19 individuals not attending. KEY OUTCOME MEASURES: Community Integration Questionnaire, Social Provisions Scale and Adult Subjective Assessment of Participation. MAIN RESULTS: The comparison group was found to consist of 12 participants who stated that they would attend a BIDC ('Yes sub-group') and seven participants who stated that maybe they would attend a BIDC but for the most part were too busy ('Maybe sub-group'). The BIDC group was found to have statistically significantly higher levels of social participation than the comparison group and particularly the 'Yes sub-group'. CONCLUSIONS: Findings provide support that attendance at BIDCs may benefit social participation. Future directions for research are suggested.

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)0.003
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.389
Teacher spread0.243 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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