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Record W4414487698 · doi:10.32799/ijih.v20i1.39522

Understanding Wholistic Health in First Nations Youth in the Context of Sport and Physical Activity

2025· article· en· W4414487698 on OpenAlexaffvenueabout
Kieran Peltier, Mark W. Bruner, Colin D. McLaren, Cindy Peltier, Brenda Bruner

Bibliographic record

VenueInternational Journal of Indigenous Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCape Breton UniversityNipissing University
Fundersnot available
KeywordsIndigenousPhysical activityContext (archaeology)Mental healthExtant taxonSport psychologyPhysical health

Abstract

fetched live from OpenAlex

Sport and physical activity have been mobilized as a vehicle for positive developmental outcomes of Indigenous youth . These experiences offer Indigenous youth the capacity to attain their full potential, accrue wholistic health benefits, and live in balance. Despite this knowledge, there is still limited research to understand Indigenous perspectives in extant literature. The purpose of this research was to explore and better understand how the wholistic health of First Nations youth is impacted through participation in sport and physical activity. Using purposeful sampling, eight First Nations youth (5 males, 3 females) between the age of 14 and 18 years (Mage = 16.75 years) were recruited to participate in one of two virtual sharing circles. Results highlighted that sport and physical activity are associated with positive outcomes related to physical adaptations, mental health, mental skills, and interconnectedness for First Nations youth. However, to balance findings, this research also demonstrates room for growth in sport and physical activity participation (e.g., racism). Findings from this research add important context and nuance to the overarching belief that sport and physical activity participation is beneficial to the wholistic health of Indigenous 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.431
Teacher spread0.318 · 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 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
Published2025
Admission routes3
Has abstractyes

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