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Record W4413561952 · doi:10.47611/jsrhs.v13i3.7246

The Accessibility of Healthcare for Indigenous Seniors in Canada

2024· article· en· W4413561952 on OpenAlexaffabout
Yining Zhang, James D. Campbell

Bibliographic record

VenueJournal of Student Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousHealth careGerontologyGeographyEnvironmental healthMedicinePolitical science

Abstract

fetched live from OpenAlex

Healthcare is an essential human right that is imperative to one’s survival. However, in Canada, accessibility of healthcare is greatly impaired for Indigenous people, and in particular Indigenous seniors. As such, this paper reviews the status quo of healthcare accessibility for Indigenous seniors in Canada, specifically examining what barriers have led to the lack of accessible healthcare, and what mitigating strategies can be implemented to alleviate the situation. Historically, the rights and culture of Indigenous Canadians have been systematically suppressed, both within and outside of the field of healthcare, leading to severe mistrust in the state and thus the healthcare system. Currently, Indigenous seniors face both tangible barriers including geography, poverty, and poor policy, and more abstract, cultural barriers, such as racism and mistrust, which contribute to lacking healthcare accessibility. To address these barriers, the overarching themes of all solutions include increasing cultural safety practices, investment in rural healthcare resources, and building trust.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
GPT teacher head0.584
Teacher spread0.383 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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