This systematic review describes the experiences of the orientation into nursing of newly graduated nurses. Systematic review. Data was collected from five databases: Medic, EBSCO Cinahl, Scopus, PsycARTICLES and ERIC (ProQuest). Qualitative, peer reviewed, original studies published in English, Swedish or Finnish before February 2016 and exploring newly graduated nurses' experiences of the nursing orientation process were included. The studies were selected by screening titles, abstracts and full texts and the quality of the studies was assessed by two researchers independently. Data was analysed using content analysis. Thirteen studies were chosen for the review. Newly graduated nurses' orientation experiences were divided into four main categories: experiences related to orientation arrangements; experiences related to the preceptor; experiencing role transition during the orientation and suggestions for changes based on orientation experiences. The findings establish that the orientation and the preceptor have a great impact on how newly graduated nurses experience the start of their career. Often, newly graduated nurses wish to continue their relationship with their preceptor after the orientation period. Results bring together both the positive and negative aspects that newly graduated nurses relate as regards their current orientation programmes. Attention needs to be paid to the variation and imbalance in the quality of orientation programmes and preceptors. A more formal form of mentorship needs to be developed further.

Healthcare professionals' digital health competence is an important phenomenon to study as healthcare practices are changing globally. Recent research aimed to define this complex phenomenon and identify the current state of healthcare professionals' competence in digitalisation but did not include an overarching outlook when measuring digital health competence of healthcare professionals. The purpose of this study was to develop and psychometrically validate two self-assessed instruments measuring digital health competence and factors associating with it. The study followed three phases of instrument development and validation: 1) conceptualisation and item pool generation; 2) content validity testing and pilot study; and 3) construct validity and reliability testing. The conceptual background of the instruments was based on individual interviews conducted with healthcare professionals (n = 20) and previous systematic reviews. A total of 17 experts assessed the instrument's content validity. Face validity was evaluated by a group of healthcare professionals (n = 20). Data collection from 817 professionals took place in spring-summer 2022 in nine organisations. Construct validity was confirmed with exploratory factor analysis. Cronbach's alpha was used to assess the internal consistency of the instruments. The instrument development and validation process resulted in two instruments: DigiHealthCom and DigiComInf. DigiHealthCom included 42 items in 5 factors related to digital health competence, and DigiComInf included 15 items in 3 factors related to educational and organisational factors associated with digital health competence. The DigiHealthCom instrument explained 68.9 % of the total variance and the factors' Cronbach alpha values varied between 0.91 and 0.97. The DigiComInf instrument explained 59.6 % of the total variance and the factors' Cronbach alpha values varied between 0.76 and 0.88. The two instruments gave valid and reliable results in psychometric testing. The instruments could be used to evaluate healthcare professionals' digital health competence and associated factors.

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 < .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.