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Record W1968681815 · doi:10.1057/jphp.2013.27

Social, cultural, and land use determinants of the health and well-being of Aboriginal peoples of Canada: A path analysis

2013· article· en· W1968681815 on OpenAlexaffabout
Shashi Kant, Ilan Vertinsky, Bin Zheng, Peggy M. Smith

Bibliographic record

VenueJournal of Public Health Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsLakehead UniversityUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsGovernment (linguistics)Mental healthPath analysis (statistics)Public healthEnvironmental healthQuality of life (healthcare)Structural equation modelingSocioeconomicsSociologyEconomic growthPsychologyMedicineEconomics

Abstract

fetched live from OpenAlex

We explored the contributions of social, cultural, and land use (SCLU) factors to Aboriginal well-being and health using path analysis and data collected from 2 of 614 First Nations in Canada. Information gathered from a structured questionnaire with questions related to seven domains of well-being and contributing factors led to key findings: (i) the SCLU domain is the most important; (ii) the most important SCLU factors are the percentage of household meals of traditional diets and the impact of government regulations on land use; (iii) the most important Health domain factors are the prevalence of mental and psychological problems and the quality of health services; and (iv) the SCLU factors of access to cultural sites, the freedom to participate in spiritual activities, and the impact of government regulations on social and cultural life have a profound effect on mental health. Improving Aboriginal well-being and health may depend on incorporating SCLU factors into new, holistic policies.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.037
GPT teacher head0.389
Teacher spread0.352 · 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 designObservational
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

Citations47
Published2013
Admission routes2
Has abstractyes

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