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Record W7132948888

Health and Wellness Impacts of Being on the Land for Indigenous Peoples in North America

2023· dissertation· W7132948888 on OpenAlexaboutno aff
Lamia Akbar

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousObesityMental healthPublic healthColonialismAssociation (psychology)Physical health
DOInot available

Abstract

fetched live from OpenAlex

Indigenous people globally face disparate chronic health outcomes when compared to non-Indigenous counterparts. In Canada, First Nations people experience higher rates of obesity and associated conditions. The causes behind this are multifactorial and include colonialism alongside various social determinants of health. Being on the land (traditional lifestyle) can have numerous benefits, including an attenuating effect on obesity associated conditions. This thesis explored being on the land in two prongs. Chapter 2 is a systematic review conducted to explore the diverse health experiences related to traditional physical activities for Indigenous youth worldwide. Overall, the literature described numerous emotional, mental and spiritual benefits of traditional physical activity, including familial and communal relationships. This research shows the importance of including traditional physical activity in future programs. Chapter 3 examined the association between selected morphometry measures and body burdens of persistent organic pollutants (POPs) and toxic metals in seven communities of Eeyou Istchee. Results indicated a negative association between cadmium with various obesity measures in both males and females. Null associations were found between POPs and morphometry.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.357
Teacher spread0.340 · 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 routes1
Has abstractyes

Explore more

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