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Record W2184053272 · doi:10.24095/hpcdp.34.2/3.03

Intentional injury hospitalizations in geographical areas with a high percentage of Aboriginal-identity residents, 2004/2005 to 2009/2010

2014· article· en· W2184053272 on OpenAlexafffundvenueabout
LN Oliver, Philippe Finès, Évelyne Bougie, Dafna Kohen

Bibliographic record

VenueChronic diseases and injuries in Canada · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsStatistics Canada
FundersHealth Canada
KeywordsIdentity (music)GeographyDemographyGerontologyMedicinePsychologySociologyArtAesthetics

Abstract

fetched live from OpenAlex

INTRODUCTION: This study describes rates of self-inflicted and assault-related injury hospitalizations in areas with a relatively high percentage of residents identifying as First Nations, Métis and Inuit, by injury cause, age group and sex. METHODS: All separation records from acute in-patient hospitals for Canadian provinces and territories excluding Quebec were obtained from the Discharge Abstract Database. Dissemination areas with more than 33% of residents reporting an Aboriginal identity in the 2006 Census were categorized as high-percentage Aboriginal-identity areas. RESULTS: Overall, in high-percentage Aboriginal-identity areas, age-standardized hospitalization rates (ASHRs) for self-inflicted injuries were higher among females, while ASHRs for assault-related injuries were higher among males. Residents of high-percentage Aboriginal-identity areas were at least three times more likely to be hospitalized due to a self-inflicted injury and at least five times more likely to be hospitalized due to an assault-related injury compared with those living in low-percentage Aboriginal-identity areas. CONCLUSION: Future research should examine co-morbidities, socio-economic conditions and individual risk behaviours as factors associated with intentional injury hospitalizations.

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)0.000
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 score0.034
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.295
Teacher spread0.289 · 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

Citations6
Published2014
Admission routes4
Has abstractyes

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