Strategies for Retaining Midcareer Nurses
Bibliographic record
Abstract
One method of reducing predicted shortages because of the aging nursing workforce is to increase retention. Few studies have examined the unique needs of midcareer nurses. A mixed-method approach including surveys and focus groups was used to identify key retention strategies and desires for midcareer nurses. Salary, benefits, positive working relationships, flexible scheduling, and the opportunity for continued education were identified as key retention strategies from this study. Registered nurses in this study reported higher perceptions of their work and work environment than licensed practical nurses did. Differences in work outcomes were evident across sectors, with community nurses reporting higher levels of job satisfaction and perceptions of work quality than nurses in acute and long-term care. Findings suggest that recruitment opportunities may exist with midcareer nurses seeking employment to return to work after time off to have a family. Proactive retention policies that focus on the needs of midcareer nurses would demonstrate a commitment and interest in keeping them in their work positions and in the profession.
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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 | 0.002 | 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".