'“As much as I miss it… I can't bring myself to go back”: Experiences of early career registered nurses who leave nursing.'
Bibliographic record
Abstract
BackgroundThere is evidence of a nursing workforce crisis with increasing intention to leave (Royal College of Nursing, 2021), yet little is known about the experience of leaving the profession or direct nursing care. Actual nursing turnover needs further exploration (Hallaran et al., 2020; Halter 2017). Early career nurse retention is a priority due to significant investment in nurse education and the high incidence of nurse turnover in the first few years of qualification (Buchan et al., 2018; Collard et al., 2020). This study explores actual nursing turnover experience in the UK context.AimTo gain in-depth understanding of the experience of leaving nursing focusing on early career nurses who worked in the NHS and left direct care nursing, or the profession itself, within 5 years of registration as a nurse.MethodsThis qualitative study uses a narrative approach to focus on individual stories of leaving nursing to elicit in-depth first-hand experiences. Participants were recruited via social media and professional networks, for this hard-to-reach group. Phase one was carried out in January-March 2024, 8 participants had in-depth online video interviews.Results and discussionPreliminary findings in phase 1 of this study offer contextual evidence and add to current knowledge based on actual turnover experiences. Numerous complex factors shape individual narratives and shared elements can inform discussion about current nursing workforce challenges. Key findings include:Becoming a nurse: motivations to nurse; strategic career choices; nursing identity.Experiences as a nurse: dissonance between values and reality of nursing; lack of autonomy to improve care; negotiating peer support; poor mental health; high-pressure workloads; COVID experiences; incivility in practice.Leaving nursing: impact of loss of nursing identity; sense of failure; reshaping personal and professional identities.ConclusionsThis UK wide study offers insight into reasons for actual professional turnover of nurses in the NHS, of interest to policymakers, NHS leaders, and researchers.ReferencesBuchan J (2018) Policy brief: Nurse Retention. International Centre on Nurse Migration. Available at: https://www.icn.ch/sites/default/files/inline-files/2018_ICNM%20Nurse%20retention.pdfCollard SS, Scammell J, and Tee S. (2020) Closing the gap on nurse retention: A scoping review of implications for undergraduate education. Nurse Education Today. 84. 104253–104253. doi.org/10.1016/j.nedt.2019.104253Hallaran AJ, Edge DS, Almost J, & Tregunno D (2020) New Registered Nurse Transition to the Workforce and Intention to Leave: Testing a Theoretical Model, Canadian Journal of Nursing Research. doi.org/10.1177/0844562120957845Halter M, Boiko O, Pelone F, Beighton C, Harris R, Gale J, Gourlay S and Drennan V (2017). The determinants and consequences of adult nursing staff turnover: a systematic review of systematic reviews. BMC Health Services Research 17. 824. doi.org/10.1186/s12913-017-2707-0Royal College of Nursing (2021) RCN Employment Survey 2021. Available at: https://www.rcn.org.uk/professional-development/publications/Employment-Survey-2021-uk-pub-010-075#detailTab
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".