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Record W2132014607 · doi:10.1136/ip.2005.009449

Young people and work injuries: an examination of jurisdictional variation within Canada

2006· article· en· W2132014607 on OpenAlexafffundabout
F. Curtis Breslin, Peter Smith, Cameron Mustard, Ran Zhao

Bibliographic record

VenueInjury Prevention · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & HealthUniversity of Toronto
FundersUniversity of Toronto
KeywordsResidenceOccupational safety and healthWork (physics)Logistic regressionInjury preventionHuman factors and ergonomicsSuicide preventionPoison controlDemographyGerontologyMultivariate analysisMedicinePsychologyEnvironmental healthSociologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to identify risk factors of work injuries among Canadian adolescents and young adults and to examine provincial differences in work injury rates. METHODS: Information on work and injuries were obtained from a representative sample of 14 541 Canadians aged 15-24 years. Respondents reported medically attended, work related injuries in the past 12 months, work hours, and type of occupation. A multivariate logistic regression on likelihood of work injury included demographic and work variables, as well as province of residence. RESULTS: Even when factors expected to vary by province such as occupation were statistically controlled, Saskatchewan youth were about twice as likely to be injured at work compared to Ontario youth. Type of job was a major correlate of injury risk, with all jobs showing higher risk than administrative clerical jobs. Even with type of job controlled, visible minorities, students, and 15-17 year olds had a reduced likelihood of work injury than their counterparts. CONCLUSIONS: Many young Canadians sustain work injuries that have clear medical costs and potential long term health consequences. Individual level explanations for youth's increased risk for workplace injuries (for example, inexperience or developmental factors) need to be supplemented with a better understanding of the broader social, economic, and political factors across jurisdictions.

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)0.000
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 score0.304
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.387
Teacher spread0.364 · 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

Citations34
Published2006
Admission routes3
Has abstractyes

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