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Record W2024036161 · doi:10.1080/17457300903564561

Youth injuries in British Columbia: type, settings, treatment and costs, 2003–2007

2010· article· en· W2024036161 on OpenAlexafffundabout
Bonnie J. Leadbeater, Shelina Babul, Mikael Jansson, Giulia Scime, Ian Pike

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsChild and Family Research InstituteUniversity of Victoria
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchHealth Canada
KeywordsInjury preventionOccupational safety and healthMedicinePoison controlRecreationSuicide preventionFalling (accident)Human factors and ergonomicsRehabilitationMedical emergencyPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

In this study, the types and costs of unintentional injuries among adolescents transitioning to adulthood are examined to provide age-appropriate prevention strategies. The data were collected in 2003, 2005 and 2007, in which a total of 273 (41%), 228 (39%) and 176 (33%) youths, respectively, reported to be having at least one serious injury. The leading types of injuries were sprains/strains, broken bones and bruises. Most injuries occurred while playing sports, falling/tripping, biking or rollerblading, mainly in recreation centres (>12-15%), schools (<27-9%), and workplaces (>2-14.5%). Most injuries were treated at emergency departments, walk-in clinics and health professional's offices (68-84%). Prevention included: doing nothing; being more careful; giving up the activity and rarely, rehabilitation or physiotherapy. The total direct cost of treatment was $471,498, (Canadian) at a mean direct cost of $775 per injury. Improved sports training and educational strategies targeted at subgroups of adolescents are needed to reduce the human and economic burden of injury.

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 score0.268
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.279
Teacher spread0.272 · 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

Citations9
Published2010
Admission routes3
Has abstractyes

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