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Record W4384341543 · doi:10.32799/ijih.v18i1.39512

Perspectives of nutrition and physical activity among families of an Indigenous Birth Cohort: a qualitative analysis exploring the barriers to and facilitators of healthy active living

2023· article· en· W4384341543 on OpenAlexaffvenue
Sujane Kandasamy, Albertha Darlene Davis, Paul Ritvo, Dipika Desai, Julie Wilson, Russell J. de Souza, Sonia S. Anand, Gita Wahi

Bibliographic record

VenueInternational Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Health and Education
Canadian institutionsBrock UniversityYork UniversityMcMaster University
Fundersnot available
KeywordsIndigenousQualitative researchCohortPsychological interventionGerontologyPsychologySociologyMedicineNursingEcologySocial science

Abstract

fetched live from OpenAlex

This is a qualitative description of the perspectives and experiences of 15 mothers from the Indigenous Birth Cohort Study as it relates to barriers and facilitators to building and sustaining healthy active living practices. Our findings illustrate six themes: 1) Systemic reinforcements of a colonial legacy; 2) Self-perceived roles as caregivers to young children; 3) Social support and family support systems; 4) Health histories (personal, family, community); 5) Locally-tailored programs and services; 6) Access to digital resources and technology. Participants also discussed solutions, which we illustrate across individual, program-level, and broader community perspectives. When suggesting or making recommendations for future interventions, programs, or new solutions, it is vital to make considerations through a lens that considers the distal (individual), intermediate (program-level), and proximal (broader community-level) barriers and facilitators for individuals with young families.

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)0.007
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 score0.105
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0020.002
Open science0.0010.003
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 head0.051
GPT teacher head0.459
Teacher spread0.408 · 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
Published2023
Admission routes2
Has abstractyes

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