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Record W2134005725 · doi:10.2105/ajph.2008.140855

Work Injury Risk Among Young People With Learning Disabilities and Attention-Deficit/Hyperactivity Disorder in Canada

2009· article· en· W2134005725 on OpenAlexaffabout
F. Curtis Breslin, Jason D. Pole

Bibliographic record

VenueAmerican Journal of Public Health · 2009
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsAttention deficit hyperactivity disorderLearning disabilityPsychiatryAttention deficit disorderAttention deficitInjury preventionPsychologyOccupational safety and healthMedicinePoison controlEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: We sought to gain a better understanding of the relationship between learning disabilities, attention-deficit/hyperactivity disorder (ADHD), and risk of occupational injury among young workers. METHODS: We assessed 15- to 24-year-old workers (n = 14 379) from cycle 2.1 of the Canadian Community Health Survey (CCHS). We gathered data on demographic characteristics, work-related factors, and presence of learning disabilities or ADHD. We conducted a multivariate logistic regression analysis to assess occurrences of medically attended work injuries. RESULTS: There was an 89% adjusted increase in work injury risk among workers with self-reported dyslexia (a type of learning disability) relative to workers reporting no learning disabilities, although this result did not meet traditional statistical significance criteria. Being out of school, either with or without a high school diploma, was associated with a significantly increased risk of work injury, even after control for a number of demographic and work-related variables. CONCLUSIONS: Our findings underscore the notion that individual differences salient in the education system (e.g., learning disabilities, school dropout) need to be integrated into conceptual models of injury risk among young workers.

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)0.001
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 score0.071
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.285
Teacher spread0.271 · 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

Citations51
Published2009
Admission routes2
Has abstractyes

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