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Record W4386572600 · doi:10.32799/ijih.v18i2.39519

Role of Self-Determination in Health and Wellness: A qualitative study with Indigenous youth health leaders across Canada

2023· article· en· W4386572600 on OpenAlexaffvenueabout
Rachel Thorburn, Jeffrey Ansloos, Sam McCormick, Deanna Zantingh

Bibliographic record

VenueInternational Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsIndigenousThematic analysisSelf-determinationInclusion (mineral)Context (archaeology)Qualitative researchLived experienceWork (physics)SociologyPsychologyGerontologyGender studiesMedicinePolitical scienceGeographySocial scienceEcology

Abstract

fetched live from OpenAlex

This work centers the voices of Indigenous young people to explore how they are defining and enacting self-determination, and how these expressions of self-determination influence the wellness of these young people and their communities. Thematic analysis was used to analyze 15 interviews with Indigenous young people about how they understood wellness in the context of their community work. Interview transcripts were analyzed to understand how young people conceptualized and enacted self-determination and its relationship to wellness, and to identify underlying connections to Indigenous self-determination theory. Results indicate that Indigenous young people define and enact self-determination through traditional healing and embracing cultural wellness practices; through sharing lived experience and meeting people where they are at; and. through community inclusion and intuitive practice. Implications for researchers, policymakers, and care providers who work with Indigenous young people are considered.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.150
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0240.011
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.390
Teacher spread0.365 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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