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Record W2085765671 · doi:10.1080/02699050903036033

Traumatic brain injuries in the construction industry

2009· article· en· W2085765671 on OpenAlexafffundabout
Angela Colantonio, Doug McVittie, John Lewko, Junlang Yin

Bibliographic record

VenueBrain Injury · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsLaurentian UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term CareOntario Neurotrauma FoundationWorkplace Safety and Insurance Board
KeywordsTraumatic brain injuryOccupational safety and healthPoison controlInjury preventionHuman factors and ergonomicsMedicineSuicide preventionMedical emergencyPhysical medicine and rehabilitationPsychiatryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study analyses factors associated with work-related traumatic brain injury (TBI), specifically in the construction industry in Ontario, Canada. METHODS: This cross-sectional study utilized data extracted from the Ontario Workplace Safety and Insurance Board (WSIB) records indicating concussion/intracranial injury that resulted in days off work in 2004-2005. RESULTS: Analyses of 218 TBI cases revealed that falls were the most common cause of injury, followed by being struck by or against an object. Mechanisms of injury and the temporal profile of injury also varied by age. For instance, a significantly higher proportion of injuries occurred in the mornings for young workers compared to older workers. CONCLUSIONS: The results of this study provide important information for prevention of TBI which suggest important age-specific strategies for workers in the construction industry.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.615
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.098
GPT teacher head0.492
Teacher spread0.394 · 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

Citations63
Published2009
Admission routes3
Has abstractyes

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