The importance of organizational commitment in rural nurses' intent to leave
Bibliographic record
Abstract
AIMS: To examine determinants of intention to leave a nursing position in rural and remote areas within the next year, for Registered Nurses or Nurse Practitioners (RNs/NPs) and Licensed Practical Nurses (LPNs). DESIGN: A pan-Canadian cross-sectional survey. METHODS: The Nursing Practice in Rural and Remote Canada II survey (2014-2015) used stratified, systematic sampling and obtained two samples of questionnaire responses on intent to leave from 1,932 RNs/NPs and 1,133 LPNs. Separate logistic regression analyses were conducted for RNs/NPs and LPNs. RESULTS: For RNs/NPs, 19.8% of the variance on intent to leave was explained by 11 variables; and for LPNs, 16.9% of the variance was explained by seven variables. Organizational commitment was the only variable associated with intent to leave for both RNs/NPs and LPNs. CONCLUSIONS: Enhancement of organizational commitment is important in reducing intent to leave and turnover. Since most variables associated with intent to leave differ between RNs/NPs and LPNs, the distinction of nurse type is critical for the development of rural-specific turnover reduction strategies. Comparison of determinants of intent to leave in the current RNs/NPs analysis with the first pan-Canadian study of rural and remote nurses (2001-2002) showed similarity of issues for RNs/NPs over time, suggesting that some issues addressing turnover remain unresolved. IMPACT: The geographic maldistribution of nurses requires focused attention on nurses' intent to leave. This research shows that healthcare organizations would do well to develop policies targeting specific variables associated with intent to leave for each type of nurse in the rural and remote context. Practical strategies could include specific continuing education initiatives, tailored mentoring programs, and the creation of career pathways for nurses in rural and remote settings. They would also include place-based actions designed to enhance nurses' integration with their communities and which would be planned together with communities and nurses themselves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".