A qualitative study of experienced nurses' voluntary turnover: learning from their perspectives
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The purpose of this research was to critically examine the factors that contribute to turnover of experienced nurses' including their decision to leave practice settings and seek alternate nursing employment. In this study, we explore experienced nurses' decision-making processes and examine the personal and environmental factors that influenced their decision to leave. BACKGROUND: Nursing turnover remains a pressing problem for healthcare delivery. Turnover contributes to increased recruitment and orientation cost, reduced quality patient care and the loss of mentorship for new nurses. DESIGN: A qualitative, interpretive descriptive approach was used to guide the study. METHODS: Interviews were conducted with 12 registered nurses, averaging 16 years in practice. Participants were equally represented from an array of acute care inpatient settings. The sample drew on perspectives from point-of-care nurses and nurses in leadership roles, primarily charge nurses and clinical nurse educators. RESULTS: Nurses' decisions to leave practice were influenced by several interrelated work environment and personal factors: higher patient acuity, increased workload demands, ineffective working relationships among nurses and with physicians, gaps in leadership support and negative impacts on nurses' health and well-being. Ineffective working relationships with other nurses and lack of leadership support led nurses to feel dissatisfied and ill equipped to perform their job. The impact of high stress was evident on the health and emotional well-being of nurses. CONCLUSIONS: It is vital that healthcare organisations learn to minimise turnover and retain the wealth of experienced nurses in acute care settings to maintain quality patient care and contain costs. RELEVANCE TO CLINICAL PRACTICE: This study highlights the need for healthcare leaders to re-examine how they promote collaborative practice, enhance supportive leadership behaviours, and reduce nurses' workplace stressors to retain the skills and knowledge of experienced nurses at the point-of-care.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".