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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 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)0.001
metaresearch head score (Gemma)0.003
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 score0.037
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.021
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.040
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 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

Citations26
Published2023
Admission routes3
Has abstractyes

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