The Reported Evidence of Nursing and Midwifery Mentorship Programmes Internationally: A Scoping Meta‐Review Providing a Comprehensive Overview of Mentorship Programmes
Bibliographic record
Abstract
AIM: To synthesise and critically analyse existing reviews of evidence on mentorship in nursing and midwifery, providing a comprehensive overview of current knowledge. DESIGN: A scoping meta-review. METHODS: The review was conducted using the scoping meta-review framework outlined by Sarrami-Foroushani et al. (2015), alongside the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines to ensure rigour and transparency. The following steps were included: (i) defining the problem, (ii) literature search and criteria, (iii) study selection and data extraction, (iv) data synthesis, (v) presentation of results and (vi) interpretation and recommendations. DATA SOURCES: A comprehensive search strategy was designed, utilising Boolean operators, truncation and predefined keywords across seven databases including MEDLINE, CINAHL, Embase, PsycINFO, Epistemonikos, ERIC and Google Scholar. RESULTS: Following double-blind screening of 269 papers, 14 literature reviews were included. The findings provided a detailed overview of mentorship programme types (formal and informal), outcome measures used to evaluate mentorship effectiveness and recommendations for future programmes. Identified themes included skill development, job satisfaction, career progression and retention outcomes, together with challenges including time constraints and balancing clinical responsibilities with mentorship roles. Formal mentorship programmes that are adequately supported and integrated into the organisational culture can improve healthcare systems, workforce stability and patient outcomes. Informal mentorship continues to offer valuable, flexible support, particularly when used alongside formal structures. Implementation challenges exist, such as time constraints, limited organisational support, and mismatched mentor-mentee pairings. CONCLUSION: This review highlights the critical role of mentorship in nursing and midwifery, offering insights into effective practices, challenges and potential areas for further research. The findings suggest that formal, structured mentorship programmes produce consistent benefits, including enhanced clinical skills, confidence and satisfaction among mentees, as well as leadership development and professional fulfilment for mentors, while positively impacting organisational efficiency and patient outcomes. Successful mentorship programmes require organisational commitment, with protected time, resources, and ongoing mentor training. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Mentorship programmes in nursing and midwifery enhance professional development, job satisfaction, and retention, enabling a stable healthcare workforce. Mentorship for mentors and mentees is linked to increased confidence, competency and readiness for advanced roles among nurses and midwives. Fostering mentorship in healthcare can lead to improved quality and continuity of care as mentees grow into more competent and confident practitioners. IMPACT: Formal mentorship programmes that are adequately supported and integrated into the organisational culture can improve healthcare systems, workforce stability and patient outcomes. Policymakers responsible for healthcare workforce development can use these findings to advocate for mentorship as a strategic investment, potentially influencing policies related to nurse retention, professional development and funding allocations for mentorship initiatives. Structured mentorship improves job satisfaction, reduces turnover and fosters professional growth, thus reducing costs associated with recruitment and training. REPORTING METHOD: The findings are reported in line with the PRISMA guidelines (Page et al., 2021) and through a narrative synthesis, summarising and analysing the results of various reviews to present a cohesive understanding of mentorship practices in nursing and midwifery. This method allowed for the integration of qualitative and quantitative findings and the identification of common themes and patterns across studies. PATIENT OR PUBLIC CONTRIBUTION: Patients or members of the public did not directly contribute to this review. However, by focusing on mentorship practices that support nurses and midwives, the study indirectly addresses public interests, as improved mentoring contributes to the quality of patient care. Future studies could benefit from patient or public feedback on desired qualities in care providers, further informing the development of mentorship programmes aligned with patient-centred care outcomes.
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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 | 0.061 | 0.197 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".