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Record W6942577053 · doi:10.14288/hfjc.v14i4.358

Impacts of COVID-19 on Indigenous Communities in Canada.

2021· article· en· W6942577053 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPandemicPublic healthSocial determinants of healthOutbreakInfographicCoronavirus disease 2019 (COVID-19)Health promotion

Abstract

fetched live from OpenAlex

Background: The coronavirus disease (COVID-19) is an infectious and potentially deadly virus with growing research examining the mechanism of outbreak and community spread. It is commonly spread through respiratory droplets. This virus has caused a global pandemic declared on March 11, 2020, which has initiated and continues to cause negative health and wellness effects worldwide. This pandemic had led to a closure of social infrastructures, local businesses, and financial instability. Purpose: The purpose of this literature review was to explore the experiences and responses to COVID-19 for Indigenous communities in Canada, specifically looking at the physical health and wellness in these communities. Methods: A literature review was conducted in June 2021. An infographic was created after a review of the literature. Results: Many Indigenous communities in Canada experience the disproportional effects from historical and ongoing health pandemics due to factors such as social determinants of health, the effects of intergenerational trauma, and systemic racism. Indigenous communities have found strengths in community-led initiatives that focus on promoting overall spiritual, mental, physical, and emotional wellbeing. Conclusion: Indigenous communities in Canada are successfully persevering through the COVID-19 pandemic by maintaining culturally relevant connections with family-centered and land-based approaches to physical health and wellness that have been practiced for generations.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0170.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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
GenreOther

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

Citations3
Published2021
Admission routes1
Has abstractyes

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