Nurse retention strategies: advice from experienced registered nurses
Bibliographic record
Abstract
PURPOSE: The purpose of the paper is to explore the insights of experienced nurses regarding initiatives they believe would effectively retain nurses like themselves in the nursing profession. DESIGN/METHODOLOGY/APPROACH: As part of a qualitative investigation into the perceptions of nurses regarding issues affecting their profession, experienced nurses were asked to describe what retention strategies they would recommend to policy-makers. A total of 16 semi-structured interviews were conducted with long-term nurses in a health region in western Canada. FINDINGS: The paper found that seven retention strategies were commonly mentioned by the participants. The qualitative mode of inquiry allowed the nurses to convey the context, attitudes and feelings behind their recommendations. RESEARCH LIMITATIONS/IMPLICATIONS: The work environments and accompanying retention policies experienced by nurses vary widely according to the specific employment context As is typical with qualitative research, the findings of this study cannot be considered as generalizable to all nurses in all health care settings. PRACTICAL IMPLICATIONS: The results of this paper provide a deeper understanding of the attitudes, emotions and contextual issues behind the nurse retention strategies seen as most appropriate by the target audience of long-term nurses. ORIGINALITY/VALUE: While there is much literature advocating the implementation of nurse retention strategies, very little evidence has been presented from a qualitative lens. It is necessary to directly listen to the voices of those impacted by policies in order to better appreciate how such policies are perceived from a bottom-up perspective.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".