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Aims: This study aimed to explore mentoring competence in nursing student mentors during clinical practice by identifying different mentor profiles and connections between different competence areas among five European countries and Japan. Methods: The study implemented a cross-sectional design in Finland, Italy, Lithuania, Slovenia, Spain, and Japan during 2016 and 2019. In total, 6208 mentors were invited, and 1862 participated from 58 healthcare organizations. The data were collected with a survey questionnaire by including background question items with the Mentor Competence Instrument. K-clustering and structural equation modeling were used for data analysis. Results: Four mentor profiles, A (43%), B (30%), C (18%), and D (9%), were identified according to the seven mentoring competence areas with high statistical significance (p < 0.001). Higher mentoring competence (mean >3.50) was observed among Finnish, Lithuanian, and Slovenian mentors with university education in nursing, older ages, more work experience, and previous education in mentoring. Lower competence (mean <2.49) was observed among Japanese and Italian mentors with diplomas in nursing, younger ages, less work experience, and no previous education in mentoring. Conclusions: Mentoring requires motivated, highly competent mentors since mentoring is a critical aspect of nursing education. Mentoring roles should be given to nurses with higher education and mentoring training. Younger, less experienced nurses without formal mentoring training may need support from senior nurses when performing mentoring roles and could also facilitate a more balanced workload between patient care and mentoring for senior nurses.

Aims: To describe the mentoring competence of clinical practice nurse mentors and identify different mentor profiles. Design: Cross-sectional research design, secondary analysis. Methods: An international, cross-sectional study design was performed in five European countries. A total of 1 604 mentors from 33 healthcare organizations participated in the study between 2016–2019. The Mentors’ Competence Instrument (MCI), which includes seven sub-dimensions and 44 items, was used to collect data. K-means cluster and binary regression analyses were performed to detect mentor profiles and determine how various factors affect competence, respectively. Results: The K-means cluster analysis identified three distinct profiles: A (n = 926); B (n = 566); and C (n = 85). The profiles showed significantly different values (p < 0.001) across all seven areas of mentoring competence. In comparison with the other profiles, nurses in profile A were older, had more work experience and were more probably to have completed mentoring-specific training.