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Record W2116083506 · doi:10.1097/ans.0000000000000039

Understanding Inequalities in Access to Health Care Services for Aboriginal People

2014· article· en· W2116083506 on OpenAlexafffundabout
Brenda L. Cameron, Maria del Pilar Carmargo Plazas, Anna Santos Salas, R. Lisa Bourque Bearskin, Krista Hungler

Bibliographic record

VenueAdvances in Nursing Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsInequalityHealth careHealth servicesMEDLINENursingSociologyMedicineEnvironmental healthEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

In Brief We present findings from an Access Research Initiative to reduce health disparities and promote equitable access with Aboriginal peoples in Canada. We employed Indigenous, interpretive, and participatory research methodologies in partnership with Aboriginal people. Participants reported stories of bullying, fear, intimidation, and lack of cultural understanding. This research reveals the urgent need to enhance the delivery of culturally appropriate practices in emergency. As nurses, if we wish to affect equity of access, then attention is required to structural injustices that act as barriers to access such as addressing the stigma, stereotyping, and discrimination experienced by Aboriginal people in this study. Findings from an Access Research Initiative to reduce health disparities and promote equitable access with Aboriginal peoples in Canada reveal the urgent need to enhance the delivery of culturally appropriate practices in emergency contexts. Achieving equity of access demands attention to structural barriers experienced by Aboriginal people in this study, such as stigma, stereotyping, and discrimination. www.advancesinnursingscience.com

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.041
GPT teacher head0.447
Teacher spread0.406 · 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
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

Citations109
Published2014
Admission routes3
Has abstractyes

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