Evidence-Based Research in Nursing Administration: The Time Is Now
Bibliographic record
Abstract
The paper "Working in Canada or the United States: Perceptions of Canadian Nurses Living in a Border Community" by Cameron, Armstrong-Stassen, Rajacich and Freeman sheds new light on the recruitment and retention factors that influence the reasons nurses select certain work environments. In a border city where Canadian nurses have a choice between working in the United States or Canada, the researchers found that full-time employment was the most important factor attracting nurses to specific institutions, followed by educational opportunities, relationships with physicians and supervisors, and scheduling that is compatible with the nurse's lifestyle. While these employment factors have been identified in the past, the research reminds us of the importance of focusing on the elements that attract nurses to specific healthcare institutions and encourage them to remain, as well as the relative importance of these factors.
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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 | 0.180 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".