Characteristics of commuting and non-commuting rural-dwelling nurses in eastern Washington
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore characteristics of rural-dwelling nurses who may or may not commute for work to an urban area. Rural communities often face a lack of healthcare provider access, including lack of access to registered nurses. With 40% of hospitals and other healthcare facilities located in rural areas, there is a critical need to obtain information about rural nurse workforce issues. Limited research has been conducted on RNs who commute to work in urban areas. To extend this research and provide more information about rural commuting nurses, a pilot study was completed in three rural counties in Washington State. Methods: A convenience sample of 72 rural-dwelling nurses was recruited through email and mail invitation. Survey data were collected using Qualtrics software. Descriptive statistics were used to determine general characteristics. Chi-square analysis was used to compare respondents who commute to those who do not. Results: Differences noted between the commuting and non-commuting nurses included non-commuters being more likely to be dissatisfied overall with their primary facility, current base salary, and salary range for their position than commuters. There were no nurses in advanced practice in the non-commuting group. Conclusions: This pilot study supports the need for further research with larger samples and in more rural counties of eastern Washington to better assess needs and characteristics of both commuting and non-commuting nurses. This information can assist rural healthcare employers to develop and implement the most effective strategies to keep the rural nurse workforce in the community.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".