Factors influencing nurse manager retention, intent to stay or leave and turnover: A systematic review update
Bibliographic record
Abstract
AIMS: To understand factors influencing nurse manager retention or intention to leave, develop a preliminary theoretical model and identify strategies and interventions for workforce planning. DESIGN: Systematic review update of literature with integrated design of mixed research synthesis. METHODS: We included peer-reviewed articles examining factors influencing retention or intention to leave for front-line, middle or patient care nurse managers. Authors independently screened articles for inclusion and assessed included articles for quality. We adhered to a convergent synthesis approach. DATA SOURCES: Nine databases included MEDLINE, EMBASE, PsychINFO, CINAHL Plus with Full Text, ERIC, Health Source Nursing/Academic Edition, Scopus, ProQuest Dissertations and Theses and LILACS in January 2023. RESULTS: Thirty-five studies published between 1990 and 2022, 22 quantitative and 13 qualitative or mixed methods, were included. 155 factors influencing nurse manager retention, intention to stay, or turnover were reported. Job satisfaction was most frequently examined (n = 7), followed by factors such as empowerment (n = 3), decision-making (n = 3) and resilience (n = 2). We developed a preliminary theoretical model demonstrating staff relations and leadership, organizational and job characteristics, socio-demographics, personal characteristics, well-being and nurse manager relationship with work influence managers' intention to stay or to leave. CONCLUSIONS: Nurse managers who were empowered, satisfied with their work, received constructive feedback and found meaning in their roles showed greater intent to stay. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurse administrators can ensure managers are provided with authority to make decisions that promote autonomy. Work cultures that allow for regular meaningful constructive feedback from staff and leaders may contribute to nurse managers feeling valued. IMPACT: Understanding factors that influence job retention or intention to leave may help nurse managers and their supervisors identify areas for strategy and intervention design to ensure sustainability of this workforce. REPORTING METHOD: PRISMA 2020 Guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".