Commuter Migration: Work Environment Factors Influencing Nurses’ Decisions Regarding Choice of Employment
Bibliographic record
Abstract
Nurse migration is of global concern for every country, and study of migration can provide critical information for managers concerned with nurse recruitment and retention. This mixed-methods research examined factors influencing registered nurses' (RNs') decisions to work in their home country, Canada, or to commute daily to a nursing position in the United States. Measures included nurses' feelings about their work environment conditions, work status congruence (the goodness of fit between employer expectations and their own regarding hours and times worked), professional development opportunities, and their perceptions of organizational support and autonomy (freedom and independence) in the workplace. All work environment variables were significantly higher for nurses working in Michigan. Qualitative results supported these survey findings, providing additional information about nurses' satisfaction. Nurses in our sample were more satisfied with all the work environment factors examined, even when stress from commuting out of country was experienced. The environmental issues examined in this study should be considered by nurse managers concerned with recruitment and retention of nurses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".