Moving On? Predictors of Intent to Leave Among Rural and Remote RNs in Canada
Bibliographic record
Abstract
CONTEXT: Examination of factors related to the retention or voluntary turnover of Registered Nurses (RNs) has mainly focused on urban, acute care settings. PURPOSE: This paper explored predictors of intent to leave (ITL) a nursing position in all rural and remote practice settings in Canada. Based on the conceptual framework developed for this project, potential predictors of ITL were related to the individual RN worker, the workplace, the community context, and satisfaction related to both the workplace and the community(s) within which the RN lived and worked. METHODS: A national cross-sectional mail survey of RNs in rural and remote Canada provided the data (n = 3,051) for the logistic regression analysis of predictors of ITL. FINDINGS: We found that RNs were more likely to plan to leave their nursing position within the next 12 months if they: were male, reported higher perceived stress, did not have dependent children or relatives, had higher education, were employed by their primary agency for a shorter time, had lower community satisfaction, had greater dissatisfaction with job scheduling, had lower satisfaction with their autonomy in the workplace, were required to be on call, performed advanced decisions or practice, and worked in a remote setting. CONCLUSIONS: The statistical evidence for predictors of ITL supported our framework with determinants related to the individual, the workplace, the community, and satisfaction levels. The importance of community makes this framework uniquely relevant to the rural health context. Our findings should guide policy makers and employers in developing retention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".