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Record W2783437674 · doi:10.1515/ijnes-2017-0064

Educating Aboriginal Nursing Students: Responding to the Truth and Reconciliation Report

2018· review· en· W2783437674 on OpenAlexaffabout
Annette Lane, Kristin Petrovic

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2018
Typereview
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNursingIndigenousCommissionCompetence (human resources)Nurse educationCultural competenceCall to actionHealth careAction (physics)MedicinePsychologyMedical educationPedagogyPolitical scienceLaw

Abstract

fetched live from OpenAlex

A 2015 Canadian report from the Truth and Reconciliation Commission issued two calls for action that specifically challenge nursing education programs: a call to incorporate indigenous knowledge and learning, and a call to reduce health disparities between aboriginals and non-aboriginals. These calls to action raise questions for nurse educators regarding how best to recruit, retain, and educate aboriginal nursing students. A literature review was conducted to examine issues faced by aboriginal students in nursing programs, as well as cultural competence with nurse educators working with aboriginal students. While there is some literature that addresses the need for aboriginal students to successfully complete nursing programs and thus be able to provide effective health care to aboriginal people, the emphasis is largely upon strategies. Although there are some exceptions, these have largely been ineffective. We argue the need to think about thinking in order to improve the effectiveness of these strategies within Canadian programs, as well as nursing programs abroad.

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)0.007
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.176
GPT teacher head0.587
Teacher spread0.411 · 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 designOther design
Domainnot available
GenreReview

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

Citations6
Published2018
Admission routes2
Has abstractyes

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