Staying in nursing: what factors determine whether nurses intend to remain employed?
Bibliographic record
Abstract
AIM: To test a model of eight thematic determinants of whether nurses intend to remain in nursing roles. BACKGROUND: Despite the dramatic increase in the supply of nurses in England over the past decade, a combination of the economic downturn, funding constraints and more generally an ageing nursing population means that healthcare organizations are likely to encounter long-term problems in the recruitment and retention of nursing staff. DESIGN: Survey. METHOD: Data were collected from a large staff survey conducted in the National Health Service in England between September-December 2009. A multi-level model was tested using MPlus statistical software on a sub-sample of 16,707 nurses drawn from 167 healthcare organizations. RESULTS: Findings were generally supportive of the proposed model. Nurses who reported being psychologically engaged with their jobs reported a lower intention to leave their current job. The perceived availability of developmental opportunities, being able to achieve a good work-life balance and whether nurses' encountered work pressures were also influencing factors on their turnover intentions. However, relationships formed with colleagues and patients displayed comparatively small relationships with turnover intentions. CONCLUSION: The focus at the local level needs to be on promoting employee engagement by equipping staff with the resources (physical and monetary) and control to enable them to perform their tasks to standards they aspire to and creating a work environment where staff are fully involved in the wider running of their organizations, communicating to staff that patient care is important and the top priority of the organization.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".