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Record W7101085779

National Aboriginal Youth Suicide Prevention Strategy Multiple Case Study of Community Initiatives

2010· article· en· W7101085779 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsProgram evaluationSuicide preventionLocal communityHuman factors and ergonomicsData collectionConjunction (astronomy)Poison controlResearch designOccupational safety and health
DOInot available

Abstract

fetched live from OpenAlex

This is a report of a collaborative research project involving members associated with NAYSPS-funded community projects.. Four sites were selected on the basis of the extent to which they might be expected to have individual and community-level impact and variation in geographical region. The sites were located in Alberta (Hobbema), Saskatchewan (Battlefords Tribal Council Indian Health Service), Québec (Uashat mak Mani-utenam) and Labrador (Nunatsiavut). The case studies were participatory; each project recruited the services of a local research mentor who worked in conjunction with members of the NAYSPS-funded program community to produce the research. Research questions varied across the four identified case sites according to the needs of the particular sites, although all studies were guided by the NAYSPS evaluation framework (Cousins & Chouinard, 2007). The study designs were all quite similar with each study relying on multiple sources of information and evidence. Given that most sites had not been collecting evaluation or project monitoring information over time, the designs were limited to retrospective cross-sectional explorations of processes and impacts. While the studies used fairly traditional

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.002
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.002
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 head0.077
GPT teacher head0.423
Teacher spread0.346 · 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 designQualitative
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

Citations0
Published2010
Admission routes1
Has abstractyes

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