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Record W4320486909 · doi:10.24095/hpcdp.43.2.05

Injuries among Canadian children and youth: an analysis using the 2019 Canadian Health Survey on Children and Youth

2023· article· en· W4320486909 on OpenAlexaffvenueabout
Chinchin Wang, Stephanie Toigo, Sarah Zutrauen, Wendy Thompson

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2023
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsPoison controlMedicineHumanitiesConcussionOccupational safety and healthInjury preventionArtMedical emergency

Abstract

fetched live from OpenAlex

This work provides an overview of injury patterns in Canadian children and youth aged 1 to 17 years. Self-reported data from the 2019 Canadian Health Survey on Children and Youth were used to calculate estimates for the percentage of Canadian children and youth who experienced a head injury or concussion, broken bone or fracture, or serious cut or puncture within the last 12 months, overall and by sex and age group. Head injuries and concussions (4.0%) were the most commonly reported, but the least likely to be seen by a medical professional. Injuries most frequently occurred while engaging in sports, physical activity or playing.

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)0.002
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 score0.011
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.040
GPT teacher head0.345
Teacher spread0.305 · 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

Citations26
Published2023
Admission routes3
Has abstractyes

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