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Record W2057746981 · doi:10.1136/ip.6.1.9

Youth injury data in the Canadian Hospitals Injury Reporting and Prevention Program: do they represent the Canadian experience?

2000· article· en· W2057746981 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInjury Prevention · 2000
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsHealth CanadaKingston General HospitalQueen's University
FundersHealth CanadaQueen's UniversityWorld Health Organization
KeywordsInjury preventionOccupational safety and healthPoison controlSuicide preventionHuman factors and ergonomicsForensic engineeringMedical emergencyInjury surveillanceMedicineEngineeringEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: Injuries to Canadian youth (11-15 years) identified from a population based health survey (World Health Organization-Health Behaviour in School-Aged Children Survey, or WHO-HBSC) were compared with youth injuries from a national, emergency department based surveillance system. Comparisons focused on external causes of injury, and examined whether similar rankings of injury patterns and hence priorities for intervention were identified by the different systems. SETTING: The Canadian version of the WHO-HBSC was conducted in 1998. The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is the national, emergency room based, surveillance program. Two hospitals involved in CHIRPP collectively provide population based data for Kingston, Ontario. METHOD: Numbers of injuries selected for study varied by data source: WHO-HBSC (n=3673); CHIRPP (n=20,133); Kingston CHIRPP (n=1944). WHO-HBSC and Kingston CHIRPP records were coded according to four variables in the draft International Classification of External Causes of Injury. Existing CHIRPP codes were available to compare Kingston and other CHIRPP data by five variables. Males and females in the three datasets were ranked according to the external causes. Data classified by source and sex were compared using Spearman's rank correlation statistic. RESULTS: Rank orders of four variables describing external causes were remarkably similar between the WHO-HBSC and Kingston CHIRPP (p>0.78; p<0.004) for mechanism, object, location, and activity). The Kingston and other CHIRPP data were also similar (p>0.87; p<0.001) for the variables available to describe external causes of injury (including intent). CONCLUSION: The two subsets of the CHIRPP data and the WHO-HBSC data identified similar priorities for injury prevention among young people. These findings indicate that CHIRPP may be representative of general youth injury patterns in Canada. Our study provides a novel and practical model for the validation of injury surveillance programs.

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.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.403
Teacher spread0.329 · 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