Work Setting, Community Attachment, and Satisfaction Among Rural and Remote Nurses
Bibliographic record
Abstract
OBJECTIVES: To describe community satisfaction and attachment among rural and remote registered nurses (RNs) in Canada. DESIGN AND SAMPLE: Cross-sectional survey of rural and remote RNs in Canada as part of a multimethod study.The sample consisted of a stratified random sample of RNs living in rural areas of the western country and the total population of RNs who worked in three northern regional areas and those in outpost settings. A subset of 3,331 rural and remote RNs who mainly worked in acute care, long-term care, community health, home care, and primary care comprised the sample. MEASURES: The home community satisfaction scale measured community satisfaction, whereas single-item questions measured work community satisfaction and overall job satisfaction. Community variables were compared across practice areas using analysis of variance, whereas a thematic analysis was conducted of the open-ended questions. RESULTS: Home care and community health RNs were significantly more satisfied with their work community than RNs from other practice areas. RNs who grew up in rural communities were more satisfied with their current home community. Four themes emerged from the open-ended responses that describe community satisfaction and community attachment. CONCLUSIONS: Recruitment and retention strategies need to include mechanisms that focus on community satisfaction, which will enhance job satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".