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Record W2130945141 · doi:10.1177/1043659608317095

Strategies to Support Recruitment and Retention of First Nations Youth in Baccalaureate Nursing Programs in Saskatchewan, Canada

2008· review· en· W2130945141 on OpenAlexaffabout
June Anonson, Joyce Desjarlais, Jackie Nixon, Lori Whiteman, Alteta Bird

Bibliographic record

VenueJournal of Transcultural Nursing · 2008
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsFirst Nations University of CanadaUniversity of SaskatchewanPrince Albert Grand Council
Fundersnot available
KeywordsNursingHealth carePopulationProgram evaluationMedicineMedical educationPsychologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

Aboriginal youth is one of the fastest growing of all populations in Saskatchewan today. This is a prime group to target for training in the health care professions. The need for strategies to support recruitment and retention in these programs is critical for maintaining our present standard and increasing demands of quality health care. Program initiatives and supports need to be implemented to encourage this population to enroll in and complete health care programs. Although only 5 years old, the University of Saskatchewan, First Nations University of Canada, and Saskatchewan Institute of Applied Science and Technology (SIAST) have created a viable northern nursing program with a retention rate of Aboriginal postsecondary students 13% greater than the provincial norm. They graduated their first class of nursing students from and for the North, May 2006.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.565
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.377
Teacher spread0.253 · 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 designNot applicable
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

Citations50
Published2008
Admission routes2
Has abstractyes

Explore more

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