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Record W2062825992 · doi:10.1080/09638280601129231

Injury among 1107 Canadian students with self-identified disabilities

2007· article· en· W2062825992 on OpenAlexafffundabout
Sudha R. Raman, William Boyce, William Pickett

Bibliographic record

VenueDisability and Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsCentre for Health Evaluation and Outcome SciencesQueen's University
FundersCanadian Institutes of Health ResearchPublic Health Agency of CanadaHealth Research Board
KeywordsMedicineInjury preventionOccupational safety and healthPhysical therapySuicide preventionHuman factors and ergonomicsPoison controlClinical psychologyPsychologyMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE: Students with disabilities are at risk for poor health outcomes; however, the causes and consequences of injury in this group are not well understood. The epidemiologies of injuries among students with and without disabilities were profiled and compared. METHODS: The cross-sectional, 2002 Health Behaviour in School-aged Children Survey, was administered to a representative sample of 7235 students (grades 6-10) from Canada. Students who reported at least one functional difficulty due to a health condition were classified as having a disability. Primary outcomes were: (i) Medically attended injury; (ii) multiple injuries, and (iii) serious injury experiences during a 12-month period. RESULTS: Some 16.3% of students reported a disability. Injuries were more common in students with disabilities compared to those without disabilities (67% vs. 51% annually, p < 0.01). Students with disabilities experienced 30% increases in the risk for medically attended injury, multiple injury, and serious injury as compared to their peers. Consistent and statistically significant associations (p < 0.05) were identified between different types of disability and all injury outcomes. CONCLUSIONS: Canadian students who report disabilities experience higher risks for injury than their peers, perhaps due to an inability to perceive and avoid environmental hazards. Injury prevention programmes are needed to address these unique risk profiles in order to prevent additional disability or secondary conditions.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.312
Teacher spread0.304 · 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.

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

Citations10
Published2007
Admission routes3
Has abstractyes

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