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Record W2807818594 · doi:10.12927/cjnl.2018.25476

Reshaping Policies to Achieve a Strategic Plan for Indigenous Engagement in Nursing Education

2018· article· en· W2807818594 on OpenAlexaffvenueabout
Lorna Butler, Heather Exner-Pirot, Lois Berry

Bibliographic record

VenueNursing leadership · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsSaskatchewan HealthUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousPoliticsAction (physics)Plan (archaeology)Relevance (law)CommissionNurse educationAction planNursingSustainable developmentStrategic planningPolitical scienceSociologyPublic relationsMedicineBusinessManagement

Abstract

fetched live from OpenAlex

Canadian universities are developing strategies to address the Truth and Reconciliation Commission (TRC) Calls to Action. There has been much attention paid to the positivist, individualistic and Eurocentric foundations of nursing and its educational curricula, but limited focus on assessing organizational structures or engaging with stakeholders. Without both approaches, the success of new initiatives may be limited. The College of Nursing at the University of Saskatchewan implemented a "Learn Where You Live" model that demonstrated a sense of place by providing access and opportunity in rural, remote and northern regions of the province. Key to this initiative was the creation of the position of Strategist for Outreach and Indigenous Engagement, whose role it is to develop strategic initiatives designed to interpret and influence socio-political and policy-level system changes. This paper shares how adding a political scientist to nursing education created an interprofessional team by introducing new ways of thinking and being that have cultural relevance and understanding for a sustainable future.

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 score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.414
GPT teacher head0.428
Teacher spread0.014 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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