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Record W4387410705 · doi:10.1177/08445621231204962

Help Wanted, Experience Preferred, Stamina a Must: A Narrative Review of the Contextual Factors Influencing Nursing Recruitment and Retention in Rural and Remote Western Canada from the Early Twentieth Century to 2023

2023· review· en· W4387410705 on OpenAlexaffvenueabout
Amanda M. McCallum, Helen Vandenberg, Kelly Penz

Bibliographic record

VenueCanadian Journal of Nursing Research · 2023
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNarrativeNursingRural areaContext (archaeology)Nurse educationRural historyPoliticsNursing shortageMedicinePolitical scienceEconomic growthSociologyHistory

Abstract

fetched live from OpenAlex

Rural and remote communities of Western Canada have struggled to recruit and retain nursing professionals since the turn of the twentieth century. Existing literature has identified the unique challenges of rural nursing due to the shifting context of rural and remote nursing practice. The objective of this narrative review is to explore the history of rural and remote nursing to better understand the contextual influences shaping rural nursing shortages in Western Canada. This narrative review compared 27 sources of scholarly and historical evidence on the nature of rural nursing practices and recruitment and retention methods following the First World War until 2023. The findings suggest that the complex nature of rural nursing practice is a consistent challenge that has intersected with the long-standing power inequities that are inherent in rural marginalization, political influences, the nursing profession, social structures, and organizational design, to perpetuate rural nursing shortages throughout the past century. Integration and collaboration are needed to reduce systemic marginalization and develop effective and sustainable solutions to reduce nursing shortages in rural and remote areas of Western Canada.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.787
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.340
GPT teacher head0.533
Teacher spread0.193 · 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
Published2023
Admission routes3
Has abstractyes

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