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Record W2924737005 · doi:10.22605/rrh4833

Keeping kids safe: caregivers' perspectives on the determinants of physical activity in rural Indigenous communities

2019· article· en· W2924737005 on OpenAlexaffabout
Lorrilee McGregor, Marion Maar, Nancy L. Young, Pamela Toulouse

Bibliographic record

VenueRural and Remote Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsIndigenousPhysical activityMedicineGerontologyNursingPhysical therapy

Abstract

fetched live from OpenAlex

INTRODUCTION: Physical activity is one way to ameliorate the disproportionately high obesity rates among Indigenous children yet little is known about the determinants of physical activity in First Nation communities. METHODS: A socioecological approach was used to explore the determinants that influence physical activity among Indigenous children in six First Nation communities in north-eastern Ontario, Canada. A thematic analysis of eight focus groups with 33 caregivers of Indigenous children was conducted. RESULTS: Caregivers reported that the present patterns of physical activity among children are different from previous generations, who were physically active through walking, outside play and physically demanding chores. Changes in lifestyles, influenced by the consequences of colonization, have resulted in reduced physical activity. Three themes emerged as present day impediments to physical activity: recreational technology, caregivers' safety concerns, and barriers to community activation. CONCLUSION: There is a dynamic interrelationship among the proximal, intermediate and distal determinants of children's physical activity with colonial policies continuing to have impacts in the participating First Nation communities. Community generated research and strategies are important ways to ameliorate physical inactivity and obesity among First Nation children.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.316
Teacher spread0.299 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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