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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".