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Record W2782687210 · doi:10.25071/ryr.v3i0.40423

Nunavut Youth Suicide Prevention

2016· article· en· W2782687210 on OpenAlexaboutno aff
Halime Çelik

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)PopulationMainlandSuicide preventionSuicide ratesPoison controlEnvironmental healthGeographyDemographyPolitical scienceMedicineSociology

Abstract

fetched live from OpenAlex

“If the populations of ‘mainland’ Canada, Denmark and the United States had suicide rates comparable to those of their Inuit populations, national emergencies would be declared” (Upaluk Poppel, 2005). Nunavut has the highest rate of youth suicide in Canada, and yet there are very few resources allocated to solving this problem. From 1993 to 1997, the suicide rate in Nunavut was 88 people per 100,000, compared with 13 for the rest of Canada. The suicide rates for Inuit men in Nunavut are ten times higher than the national rate in Canada. Inuit youth end their lives for various reasons, some of which are preventable. This research investigates the potential causes of suicide in Nunavut and analyzes various factors such as colonization, residential schooling, poor parenting, violence, and alcohol abuse. Initiatives by both government and non-governmental organizations have been aimed at lowering suicide rates by, for example, limiting the means of suicide. Suicide rates in the UK were drastically reduced when the government replaced the use of toxic coal with non-toxic natural gas for domestic use, so people could no longer poison themselves by putting their heads in the gas ovens. I argue that, although this may be a way to reduce suicide rates, it is a temporary solution which does not address the root causes of the problem. I make policy recommendations for some of the problems that the Nunavut population currently faces.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.171
GPT teacher head0.446
Teacher spread0.274 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2016
Admission routes1
Has abstractyes

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