Working in Canada or the United States: Perceptions of Canadian Nurses Living in a Border Community
Bibliographic record
Abstract
Recruitment and retention of registered nurses is a critical issue facing nursing leaders. Global shortages of nurses have been projected over the next decade. This study used the theoretical framework of push and pull factors to identify influences on nurses' decision to select work in either their home community or a cross-border community, when that opportunity was available to them. Registered nurses living along the southwest border of Ontario were identified with the assistance of the College of Nurses of Ontario (CNO) and surveyed to determine the factors that influenced their decision to work in Canada or the United States, as well as their intent to remain in their current workplace. Measures included demographic information, reasons for selection of employment, and work environment factors relating to nurses' jobs, work relationships, scheduling/staffing, workload and attachment to their current place of employment. MANCOVA was used to examine differences between the two groups controlling for age, organizational tenure and employment status. Full-time employment was the greatest push factor identified by RNs, and nurses working in the United States were also more satisfied with the pull factors of development opportunities, relationships with physicians and supervisors, and scheduling congruence. Recommendations for recruitment and retention are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".