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Record W2041275952 · doi:10.1076/icsp.9.2.73.8701

Characteristics and risk factors for accident injury in Canada from 1986 to 1996: an analysis of the Canadian Accident Injury Reporting and Evaluation (CAIRE) database

2002· article· en· W2041275952 on OpenAlexaffabout
Frank Mo, Bernard C. K. Choi, Clarence Clottey, Barbaba LeBrun, Glenn Robbins

Bibliographic record

VenueInjury Control and Safety Promotion · 2002
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsHealth Canada
Fundersnot available
KeywordsMedicineInjury preventionOccupational safety and healthPoison controlSuicide preventionPopulationInjury surveillanceHuman factors and ergonomicsEnvironmental healthMedical emergencyDatabasePathology

Abstract

fetched live from OpenAlex

This study analyzed the database of Canadian Accident Injury Reporting and Evaluation (CAIRE) for the injuries reported from January 1986 to March 1996 in seven provinces at children's or general hospitals in Canada. In order to describe the characteristics of injuries, we compared the different categories of injuries by sex and by age groups, identified patterns of injuries, and detected the products causing injury to Canadian people. The results showed that there were 130,489 injury cases in Canada during the 10 years from 1986 to 1996. The 10-19 year age group had 57,582 cases, representing 44.13% of total injuries, and making it the group with the highest occurrence of injuries. The male injury rate (69.75%) was significantly higher than the female rate (30.25%) (P = 0.0001). Six areas were identified as priorities for intervention: 1) injuries occurring on playgrounds among children and youth; 2) sports and playground apparatus injuries and injuries sustained in transit among young people; 3) the top five causes of injuries; 4) diagnosis and treatment of injuries; 5) consumer products and safety; and 6) nature and physical sites of injuries. Further work is needed in: evaluating injury causes, comparing the results with reports from other countries and the necessary approaches and prevention measures to reduce and control injury occurrences to improve the quality of consumer products, and to protect the health of the population in Canada.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.068
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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

Citations14
Published2002
Admission routes2
Has abstractyes

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