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Record W2334861032 · doi:10.1177/1077800416629695

First Nation and Métis Youth Perspectives of Health

2016· article· en· W2334861032 on OpenAlexaffabout
JoLee Sasakamoose, Andrea Scerbe, Ila Wenaus, Amanda Scandrett

Bibliographic record

VenueQualitative Inquiry · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsIndigenousPsychological resilienceQualitative researchSociologyYouth studiesResilience (materials science)Public relationsPsychologyGender studiesSocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article describes an Indigenous and qualitative research project with 13 First Nation (FN) and Métis youth attending an Aboriginal youth health and wellness program located in the Canadian prairies. Our goal was to collaborate with the youth to co-create knowledge concerning their definitions of health using a convergence of Indigenous and qualitative methodologies. Independent but interconnected themes that emerged are discussed as related to neurodecolonization and the recovery of traditional practices and their contribution to youth resilience. The resilience of youth was reflected in these themes as well as their definitions of health. Our findings point to the importance of acknowledging and validating the role that neurodecolonization practices contribute to healing, both at individual and collective levels. Furthermore, we suggest recognizing resilience as well as viewing health holistically to more adequately understand and address the health-related concerns of FNs, Métis, and Inuit (FNMI) youth.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.815
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0050.002
Open science0.0010.006
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 head0.146
GPT teacher head0.435
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 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

Citations42
Published2016
Admission routes2
Has abstractyes

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