Organizational culture and nurse’s turnover: A systematic literature review
Bibliographic record
Abstract
Background: Nurses turnover is a current and international problem which is closely related to the organizational culture. Despite being widely discussed, the evidence available in the literature is dispersed and most studies only concern specific health contexts and sectors. The aim of this study is to identify scientific evidence on the factors of organizational culture associated with nurses turnover.Methods: A systematic literature review was carried out between January 2014 and December 2018. The methodological quality of the articles was assessed through the Joanna Briggs Institute and Registered Nurses Association of Ontario guidelines.Results: Nurses’ turnover in healthcare organizations is complex and multifactorial. The evidence shows individual and organizational factors that influence nurses’ turnover. Some retention strategies to reduce this phenomenon were also identified in literature.Conclusions: Nursing managers should seriously consider the problem of nurses’ turnover, as it affects the productivity and quality of care provided in health organizations. By working the factors associated with organizational culture, organizational climate and leadership, it will be possible to reduce nurses’ turnover rates in different healthcare contexts. In the development of public policies, decision-makers should take into account two fundamental aspects: the needs and expectations of the population; and the stability of professional groups. It is suggested to investigate this issue in Portugal.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".