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Record W2606329910 · doi:10.7870/cjcmh-2017-004

“Our Next Generation”: Moving Towards a Surveillance and Prevention Framework for Youth Suicide in Saskatchewan First Nations and Métis Populations

2017· article· en· W2606329910 on OpenAlexaffvenueabout
Caroline L. Tait, Peter Butt, Robert Henry, Roger Bland

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of AlbertaUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsCoronerIndigenousSuicide ratesEthnic groupSuicide preventionPopulationDemographyGeographySuicide methodsPoison controlEnvironmental healthPolitical scienceMedicineCriminologyPsychologySociologyLaw

Abstract

fetched live from OpenAlex

Suicide is a concern for many Indigenous communities in Canada. Suicide rates in Canada have decreased but rates in Saskatchewan remain above the national average. In northern Saskatchewan, where First Nations and Métis people make up 85% of the population, suicide rates are well above the national rate. The majority of reported suicides in this region are committed by adolescents and young adults. However, the current rate of suicide among First Nations and Métis youth in Saskatchewan cannot be determined because ethnicity is not tracked on hospitalization and coroner records. Our study argues this is a barrier to suicide prevention in Indigenous populations and recommends improvements to the design and implementation of suicide surveillance and prevention strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0100.004
Scholarly communication0.0070.005
Open science0.0050.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.224
GPT teacher head0.420
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2017
Admission routes3
Has abstractyes

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