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Record W3113580704 · doi:10.1111/nin.12394

Nurses as agents of disruption: Operationalizing a framework to redress inequities in healthcare access among Indigenous Peoples

2020· article· en· W3113580704 on OpenAlexaffabout
Tara C. Horrill, Donna Martin, Josée G. Lavoie, Annette Schultz

Bibliographic record

VenueNursing Inquiry · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRedressOperationalizationHealth careEquity (law)Health equityIndigenousPublic relationsNursingContext (archaeology)SociologyPolitical scienceMedicineGeographyLaw

Abstract

fetched live from OpenAlex

Health equity is a global concern. Although health equity extends far beyond the equitable distribution of healthcare, equitable access to healthcare is essential to the achievement of health equity. In Canada, Indigenous Peoples experience inequities in health and healthcare access. Cultural safety and trauma- and violence-informed care have been proposed as models of care to improve healthcare access, yet practitioners lack guidance on how to implement these models. In this paper, we build upon an existing framework of equity-oriented care for primary healthcare settings by proposing strategies to guide nurses in operationalizing cultural safety and trauma- and violence-informed care into nursing practice at the individual level. This component is one strategy to redress inequitable access to care among Indigenous Peoples in Canada. We conceptualize barriers to accessing healthcare as intrapersonal, interpersonal, and structural. We then define three domains for nursing action: practicing reflexivity, prioritizing relationships, and considering the context. We have applied this expanded framework within the context of Indigenous Peoples in Canada as a way of illustrating specific concepts and focusing our argument; however, this framework is relevant to other groups experiencing marginalizing conditions and inequitable access to healthcare, and thus is applicable to many areas of nursing practice.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.348
GPT teacher head0.544
Teacher spread0.196 · 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.

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

Citations20
Published2020
Admission routes2
Has abstractyes

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