This scoping review used Arksey and O’Malley [25] five-stage framework: (1) identification of the research question; (2) identification of relevant studies; (3) selection of studies; (4) mapping the data; and (5) gathering, summarizing, and reporting the results. The reporting of the review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) [26]. The review’s protocol was not registered or published.

What is known about the use of technology-supported guidance models to stimulate nursing students’ self-efficacy in clinical practice?

A systematic search was conducted on December 13, 2021, and was updated on April 21, 2023, in the following databases: MEDLINE All (Ovid), PsycINFO (Ovid), Embase (Ovid), ERIC (EBSCOhost), CINAHL (EBSCOhost), and Web of Science Core Collection. The search strategy consisted of three main topics: (1) self-efficacy, (2) technology, and (3) nursing students. Based on these 3 topics, we chose search terms using Medical Subject Headings terms and text words. The search strategy was built in MEDLINE by a research librarian, peer reviewed by a second research librarian, and then adapted to the other databases (Multimedia Appendix 1). Moreover, we hand-searched the reference lists of the included papers to assess whether any of the studies mentioned in those references were pertinent to our review. Furthermore, we conducted forward citation searching using the Google Scholar platform to identify relevant studies that had cited the included papers.

The research librarian exported the identified citations to EndNote (Clarivate) to remove duplicates [27]. Subsequently, the citations were exported to Rayyan (Rayyan Systems Inc) [28] for storage, organization, and blinding of the study selection process. Based on the eligibility criteria (Textbox 1) [29], PB and AAGN independently conducted a pilot test of 10% (380/3804) of the citations to screen titles and abstracts, and the eligibility criteria were not modified. Pairs of authors independently screened titles and abstracts to evaluate whether full-text studies met the eligibility criteria (PB+AAGN, Fernando Riegel+JGM, and SAS+JZ in the first search round and PB+JGM and SAS+HMB in the update search round). When doubt arose about a full-text study’s inclusion, a third author independently evaluated the full-text study. The decision was based on a negotiated consensus, and the reasons for excluding full-text studies were recorded.

The same pairs of authors that selected the studies extracted data from the included studies. One extracted the data, maintaining the wording and terminology of the studies, and the other checked data accuracy against the studies using a standardized data charting form that included the following information as recommended by the Joanna Briggs Institute [30]: authors, year, country, study objective, population and sample size, research focus or technological models, design, outcomes measures (related to self-efficacy), and findings.

In line with the framework by Arksey and O’Malley [25], a critical appraisal of the methodological quality or risk of bias of the included studies was not performed.

PB and AAGN used an inductive approach to analyze and thematically organize the data from the included studies [25]. The data were extracted from the studies’ findings sections and were read several times to identify patterns of similarities and differences across the studies related to our research question. These patterns were organized into thematic groups using a low level of abstraction. Next, the preliminary thematic groups were discussed with the rest of the research team and a consensus was achieved [31-33].

The database search identified 9408 records, of which 5604 were duplicates, so we screened the titles and abstracts of 3804 records. A total of 33 studies were evaluated for eligibility and 8 studies described in 8 publications were included. We did not find any relevant additional studies through hand searches of the reference lists or forward citations of the included studies. Figure 1 provides an overview of the study selection process and the reasons for the exclusion of full-text reports.

The included studies were conducted in Taiwan (n=2) [34,35], South Korea (n=2) [36,37], China (n=2) [36,38], Norway (n=1) [39], and Finland (n=1) [40]. In all, 5 studies used a quantitative method and had an experimental design [37-41], and 3 studies used multiple methods [34-36].

The sample size of the studies ranged from 36 to 171 participants, the majority age range (721/770, 93.6%) across the studies was 20-30 years, and most of the participants were female (508/564, 90%). A total of 3 studies did not report the sex of the participants [34,35,37]. In most of the studies (7/8, 88%), the clinical practice was performed in a hospital [34,36-41], whereas 1 study’s clinical practice was performed in home care [35]. Table 1 provides a detailed overview of the studies’ characteristics and Multimedia Appendix 2 [34-41] provides a description of interventions, duration, and frequency for each study included.

To answer the research question, the results were organized into four thematic groups: (1) technological solutions for learning support, (2) learning focus in clinical practice, (3) teaching strategies and theoretical approaches for self-efficacy, and (4) assessment of self-efficacy and complementary outcomes. Textbox 2 provides an overview of the content covered within the thematic groups.

Various technological solutions to support the development of self-efficacy were identified across the included studies, such as a chatbot with artificial intelligence [34], online chat [36], a mobile app with simulation [39,41] and without it [40], a mobile phone [38], and a digital platform [35,37]. The technological tools required internet access to function for the intended purpose. In 7 studies [34-36,38-41], the participants had access to the technological solution on their smartphones anywhere and at any time. The participants used the technical solutions to perform learning activities [34,35,37,39-41] to interact with colleagues [34-36] and to communicate with nurse educators [35,38,40]. In 1 study, the technological solution (with e-learning content) was used as a preparatory learning activity before clinical practice [37].

The included studies focused on specific learning situations in clinical practice, such as nursing procedures [34,39,41] and nursing areas [35,37,40]. Regarding nursing procedures, 1 study provided educational knowledge about infectious diseases and vaccine administration [34], while the other studies included learning situations about vital signs, intravenous injection, gastric lavage, endotracheal suction [41], and physical assessment skills [39]. Regarding nursing areas, the research focus was surgical nursing [37], management and communication [40], and home care [35]. One study investigated students’ mental health [38] and another investigated students’ professional identity [36].

Nurse educators were the main facilitators of the technology-supported guidance models. A total of 4 studies applied various teaching strategies, such as educators’ feedback on nursing students’ learning activities [35,36,38,40] and a flipped classroom [37]. Three of the studies used different theoretical approaches, such as attention, relevance, confidence, satisfaction (ARCS) theory [41], Tajfel’s social identity theory (SIT) and career self-efficacy theory (CSET) [36], and the Fundamentals of Care (FoC) framework [39]. One study did not apply teaching strategies or theoretical approaches [34].

Strandell-Laine et al [40] and Wu and Sung [35] designed an app to support clinical learning by stimulating communication between nursing students and nurse educators. In the intervention by Strandell-Laine et al [40], the learning content included the schedule of clinical practice, learning objectives, a learning diary, and midterm and final evaluations. In the study by Wu and Sung [35], students accessed information, uploaded data, posed questions, and discussed the learning situations with nurse educators. In both studies, the intervention content and nursing students’ clinical practice experience were the basis of feedback elaboration delivered by the educators. Nursing students also received feedback from nurse educators in Wang et al [38] study, but the focus was on psychological support. The intervention was delivered in three modules: (1) support (the participants were asked to write a paragraph describing their “happy experiences” during their clinical practice sessions); (2) education (2 clinical educators provided weekly lectures on topics to improve clinical and communication skills, find happiness in daily life, build confidence when caring for patients, manage stress and pressure, perform self-care while caring for others, and build social support); and (3) reflection (participants were encouraged to describe stressful situations during their clinical practice to their clinical educators, and the educators helped the participants analyze the situations and provided tailored advice for handling similar situations in the future). In the study by Lee and Park [37], the flipped classroom was used as a teaching strategy, including instructor guidance before clinical training, on-site instruction during clinical practice, and a case conference after the end of clinical practice.

The study by Chang et al [34] used an artificial intelligence–based app developed with natural language processing to encourage nursing students to ask questions or use a pop-up menu to search for needed information in addition to discussing medical issues with their peers and with the chatbot. Kim and Suh [41] used an app flowchart based on the ARCS theory with four phases: (1) the attention phase stimulated the participants’ motivation to learn; (2) the relevance phase helped the participants to think about which items they should prepare for each nursing skill and devise a care plan for the patients and themselves; (3) confidence phase; and (4) satisfaction phase, the participants learned interactively by answering messages and quizzes that popped up on their mobile screen. Egilsdottir et al [39] used the FoC framework divided into three main areas: (1) the nurse-patient relationship, (2) integration of care, and (3) contextual factors The study used the basic physical assessment skills (B-PAS) to measure students’ performance and used the FoC framework to assess the student (nurse)–patient relationship. Wang et al [36] based their training program on the combined SIT and CSET. The program was designed on the basis of SIT’s 3 phases of how social identity is built and modified, including social categorization, social comparison, and positive distinctiveness; the intervention elements embedded in the 3 phases were derived from the CSET, comprising direct experience, substituted experience, physio-psychological condition, and social persuasion.

Three studies [37,40,41] measured self-efficacy using the self-efficacy in clinical performance (SECP) instrument [44], which comprises 37 self-assessed items on an 11-point Likert scale in five domains: (1) assessment, (2) diagnosis, (3) planning, (4) implementation, and (5) evaluation.

Two studies [34,38] measured general self-efficacy with 2 different instruments. Chang et al [34] used a self-efficacy instrument constructed by Pintrich et al [42] that includes 8 self-assessed items with 5-point Likert scales. Wang et al [38] used a self-efficacy scale developed by Jerusalem and Schwarzer [45] with 10 items on a 4-point Likert scale in four domains: (1) strategic, (2) contingency, (3) motivational, and (4) executive effectiveness.

Wang et al [36] measured nursing students’ professional self-efficacy with a 27-item questionnaire on a 5-point Likert scale including six factors: (1) professional attitude and belief, (2) problem-solving ability, (3) professional information collection and professional planning capacity, (4) professional cognition, (5) professional value, and (6) professional choice.

Egilsdottir et al [39] created a questionnaire with 13 items using a 7-point Likert scale to map nursing students’ perceived confidence related to the examination techniques in B-PAS, which are inspection, palpation, percussion, and auscultation. The questionnaire items were formulated in line with Bandura’s [46] description of self-efficacy.

Wu and Sung [35] revised and applied a computer self-efficacy questionnaire designed by Compeau and Higgins [43] with 24 items on a 5-point Likert scale in three dimensions: (1) computer use experience, (2) computer self-efficacy, and (3) computer anxiety.

In all these self-efficacy instruments, higher scores indicate greater self-efficacy. All the studies measured self-efficacy along with other outcomes, such as basic knowledge [34,41], nursing skills performance [39,41], self-leadership and social problems [37], and professional competence [39,40]. Three studies measured anxiety [35,36,38], 2 measured stress [36,38], 1 measured professional identity [36], and another measured system satisfaction [35]. Furthermore, 3 studies [34-36] conducted interviews to gather qualitative data, exploring participants’ perceptions of their feelings, experiences, influencing factors, their understanding of technology’s impact on learning experiences in nursing education, and suggestions for improving the intervention.

In all the studies, the self-efficacy scales and other instruments showed improvement in the posttest within the groups. However, 1 showed statistically significant differences between the experimental groups and the control groups [34].

This scoping review aimed to systematically map and identify gaps in published studies on the use of technology-supported guidance models to stimulate nursing students’ self-efficacy in clinical practice. The database search identified 3804 citations, but only 33 studies were assessed for eligibility, of which 8 were eligible for inclusion, which may indicate a research gap on the phenomenon of interest. Although the number of studies was small, the technological solutions were diverse, which was not surprising, as it aligns with the findings of a systematic mixed studies review that investigated technology-supported guidance models to stimulate critical thinking [23]. The use of technology to support clinical practice appears to be a relatively new research field, characterized by frequent experimentation. Technological advancement in nursing education has greatly increased, especially during the COVID-19 pandemic [47].

Our findings suggest that technological solutions were organized as technology-supported guidance models with a predefined set of nursing procedures or nursing areas. Nurse educators provided guidance with the support of technological solutions, stimulating nursing students’ active learning. This pedagogical approach diverges from traditional education and seeks to transcend teaching based on the unilateral transfer of content so as to stimulate creative, critical, and transformative practices [48]. Active learning is student centered and interactive and offers feedback that meets the student’s learning needs [49]. Although only 1 of the included studies [39] provided information on the development of the technological solutions, they seemed to be tailored to meet the individual nursing students’ needs for specific knowledge and competence. It would be valuable to ascertain whether the users of the other 7 included studies were actively engaged in the development process due to the potential positive impact of such involvement. Nes et al [50] underscore the significance of incorporating all stakeholders (ie, nursing students, nurse preceptors, and nurse educators) as users during the creation of a technology-supported guidance model. This approach is essential to guarantee that the technological solution aligns with the expected quality standards to meet users’ needs and achieve the anticipated educational outcomes in clinical practice.

Half (4/8, 50%) of the included studies applied teaching strategies without a theoretical approach in their technology-supported guidance models, which is in line with the findings of a previous review [23]. The lack of a theoretical approach may make it challenging to explain study findings [51]. Despite positive findings, only 1 of the included studies showed statistically significant effects regarding self-efficacy. Applying a theoretical approach in intervention studies seems to be associated with positive findings and large effect sizes [52]. Therefore, technology-supported guidance models that apply pedagogical theory intended to stimulate self-efficacy may have a better chance of success [53]. A nonsignificant effect in the included studies that used a theoretical approach may result from an insufficient sample size or insufficient duration of intervention. Despite the lack of statistical significance in most studies, the observed effects were consistently positive, aligning with findings in other studies using technological solutions [54-56]. Consequently, interventions integrating technology have the potential to contribute positively to student learning outcomes.

Furthermore, Linnenbrink and Pintrich [57] found evidence for a conceptual framework that demonstrated the connection between motivation, self-regulation, and academic learning and that these connections were not confined solely to the theoretical classroom context but also extended to clinical practice. Motivational factors and cognitive processes may interact in intricate ways to facilitate student learning [57,58].

Aligning with previous research [59,60], our findings underscore the significant role of nurse educators as the primary facilitators of technology-supported guidance models. Nurse educators share responsibility for fostering nursing students’ self-beliefs, as these self-beliefs can have positive or negative influences on their performance [61]. Educators and institutions are responsible for helping students develop their competence and confidence as they progress in their studies [62]. However, technological tools should not be incorporated into guidance models in isolation, as such incorporation also requires oversight, support, and mentorship from not only nurse educators but also nurse preceptors and peers optimizing the impact of technology on the educational experience [63].

Only 3 of the included studies used the SECP instrument to assess the self-efficacy of nursing students. Using the same instrument, such as the SECP, facilitates replication and enables comparisons across studies investigating similar outcomes [44,64,65]. By contrast, the use of different instruments to measure the same outcome makes it challenging to compare findings across studies, conduct meta-analyses, and establish standardized thresholds or reference points for specific outcomes [66].

Our findings show that the studies also incorporated other assessment tools. This could be because of the strong correlation between self-efficacy and factors such as motivation [67], satisfaction [63,68], academic achievement [58,69], and student persistence [70]. Furthermore, it is important to acknowledge that other outcomes could be essential in addressing the research questions posed by these studies.

The strengths of our review are the acknowledged methodological framework for conducting a scoping review, the comprehensive database search, and the systematic process by which pairs of authors independently assessed eligibility and extracted data. Furthermore, the data were analyzed by 2 authors and discussed with the rest of the research team, facilitating credibility, dependability, and intersubjectivity.

We tried to include all possible synonyms of the concept of self-efficacy and similar concepts in our search strategy, but due to the multidimensional nature of self-efficacy, we may have overlooked some synonyms. Our review also had some language restrictions. Consequently, we may have been unable to identify some relevant studies. Another limitation may be that the review protocol was not published. However, the eligibility criteria and search strategy were determined before the study selection process was carried out.

Diverse technological solutions were used in guidance models to stimulate nursing students’ self-efficacy in clinical practice. Even though these interventions showed positive outcomes, they were not statistically significant. This underscores the need for further refinement by tailoring technological tools to meet user needs, making stakeholder involvement essential, and implementing interventions that are developed on the basis of a theoretical approach, as well as applying teaching strategies with a theoretical approach. Although nurse educators are vital for students’ development, the contributions of nurse preceptors and peers should not be underestimated.

Our findings show that a variety of instruments are used to assess self-efficacy and that not all such instruments have been validated. Consequently, future studies should use validated instruments to ensure relevance and enable meaningful comparisons of self-efficacy across studies.