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Record W2265069135 · doi:10.1093/pch/17.7.393

Preventing unintentional injuries in Indigenous children and youth in Canada

2012· article· en· W2265069135 on OpenAlexaffabout
Anna Banerji

Bibliographic record

VenuePaediatrics & Child Health · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsHealth CanadaFirst Nations Health and Social Secretariat of ManitobaAssembly of First Nations
Fundersnot available
KeywordsIndigenousTollMedicineMultidisciplinary approachHealth careInjury preventionSuicide preventionOccupational safety and healthPoison controlEnvironmental healthMedical emergencyNursingEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Unintentional injuries are the leading cause of death in Canadian Indigenous children and youth, occurring at rates three to four times the national average. Death and disabling injuries not only devastate families and communities but take a heavy toll on health care resources. The lack of statistics, ongoing surveillance or injury prevention programs for Indigenous children and adolescents further compound human and health care costs. Indigenous communities are heterogeneous culturally, in terms of access to resources, and even as to risks and patterns of injury. Yet in general, they are far more likely to be poor, to have substandard housing and to have difficulty accessing health care, factors which increase the risk and impact of injury. There are urgent needs for injury surveillance, research, capacity-building, knowledge dissemination, as well as for injury prevention programs that focus on Indigenous populations. Effective injury prevention would involve multidisciplinary, collaborative and sustainable approaches based on best practices while being culturally and linguistically specific and sensitive.

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 score0.105
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.283
Teacher spread0.270 · 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

Citations21
Published2012
Admission routes2
Has abstractyes

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