MétaCan
Menu
Back to cohort
Record W2097107770 · doi:10.3109/13668250.2011.625927

Injury incidence and patterns in workers with intellectual disability: A comparative study

2011· article· en· W2097107770 on OpenAlexafffundabout
Rosemary Lysaght, Cynthia Sparring, Hélène Ouellette‐Kuntz, Carrie Anne Marshall

Bibliographic record

VenueJournal of Intellectual & Developmental Disability · 2011
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsRoyal Ottawa Mental Health CentreQueen's University
FundersWorkplace Safety and Insurance Board
KeywordsIntellectual disabilityWorkers' compensationOccupational safety and healthOccupational injuryJurisdictionIncidence (geometry)Injury preventionCompensation (psychology)MedicineHuman factors and ergonomicsPoison controlPsychologyEnvironmental healthPsychiatrySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Workplace safety is a concern in the employment of persons with intellectual disability, due to both real concerns for employee well-being, and the effect that negative perceptions of safety risk can have on hiring. METHOD: This study involved a retrospective analysis of workplace insurance claim records for workers with and without disability in a Canadian social enterprise. RESULTS: Workers with intellectual disability sustained fewer injuries and experienced fewer absences due to injury than workers without disability. Lost-time injury rates for this business were not significantly different from those reported by other employers in the jurisdiction. CONCLUSION: Workplace safety is a concern for all workers, but fear of increased injury rates and heightened compensation costs should not be perceived as a risk when hiring individuals with intellectual disability.

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 score0.367
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.344
Teacher spread0.261 · 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

Citations13
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Intellectual & Developmental DisabilitySame topicDown syndrome and intellectual disability researchFrench-language works237,207