Do we really understand how to retain nurses?
Bibliographic record
Abstract
AIM: To compare views of nurse executives with those of nurses who have left the profession on the importance of retention strategies. BACKGROUND: Although much has been written about nursing turnover, there continues to be dissonance among decision makers as to why nurses leave the profession and what the most crucial issues are for retention. METHOD: Factor analysis was undertaken to compare responses of nurse executives with those of nurses employed outside of nursing. RESULTS: Contract requirements represented the greatest discrepancy, 1.07, followed by legal and employer issues, 1.02; worklife/homelife balance, 0.91; external values and beliefs about nursing, 0.75; and professional practice, 0.29. CONCLUSIONS: A disparity exists between perceptions of nurse executives and those of nurses who have left the profession as to which issues are most critical in retention. We suggest that nurse executives ensure sufficient organizational support for nursing unit managers who are more likely to understand methods of retaining nurses at the clinical interface.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".