Generic competence among health sciences students in higher education – A cross-sectional study

Background: Highly competent health care experts are needed for the development of the social and health care sectors. More knowledge is needed on the levels of generic competencies that health sciences experts possess, particularly in the context of complex decision-making. Objectives: To describe self-evaluated generic competence of health sciences students and its associated factors. Design: A cross-sectional observational study design. Participants: A total of 291 health science students in five universities in Finland participated in this study during the spring of 2022. Methods: The data was collected by using the HealthGenericCom instrument with 88 items and 8 sum dimensions using a five-point Likert scale (1 — poor to 5 — excellent): 1) competence in leadership, administration, and finance; 2) people-centred guiding competence; 3) competence of health promotion; 4) competence of evidence-based practice; 5) digital competence; 6) competence in work well-being and self-management; 7) competence in collaboration and problem-solving, and 8) competence in societal interaction. The K-means cluster algorithm was used to classify generic competence profiles to identify the profiles of health sciences students. Results: Four generic health sciences competence profiles (A = 18 %, B = 23 %, C = 33 %, D = 26 %) were identified. Profile A demonstrated the lowest level of most generic competencies in health sciences. Digital competence was shown to be at the lowest level among the participants, whereas competence in collaboration, problem-solving, and health promotion was evaluated as the highest competence level. The students evaluated their competence as being higher when they were older, were currently engaged in master ’ s degree programmes, had completed work-based practical training in social and health care, and had varied work experiences or held leading positions. Conclusions: Students need to improve their generic competencies in health sciences, with a particular focus on developing their digital competence. More focus should be given to work-based practical training.


Introduction
Evidence-based health care requires experts and professionals (Jordan et al., 2019;World Health Organization (WHO), 2021;WHO, 2022).They should have the competencies to develop people-centred, equitable, and high-quality care in a digitalising society in collaboration with others as well as to apply knowledge in practice, for example, through leadership positions (Gallagher-Ford and Connor, 2020).Higher education in health sciences produces experts with the competence to take responsibility for their own and others' actions in developing health care (Gallagher-Ford and Connor, 2020;WHO, 2022).Competence can be defined as knowledge, skills, and attitudes and is considered a holistic entity (Cowan et al., 2007).
Focus on general competence is a crucial issue in ensuring the quality and safety of care.The concept of generic competence has emerged in the international debate on the competencies required in working life (Al Jabri et al., 2021;Langins and Borgermans, 2015;Sarkar et al., 2021;Strijbos et al., 2015).The concept is used in parallel with twenty-firstcentury skills, which include, for example, collaboration, critical thinking, and problem-solving skills (Binkley et al., 2012;Organisation for Economic Cooperation and Development (OECD), 2018;Tuononen et al., 2022;van Laar et al., 2020).Several studies have assessed the competence of health professionals from various perspectives (e.g.Al Jabri et al., 2021), including general competencies in higher education (Clark et al., 2016;Tuononen et al., 2022;Ursin et al., 2021).Few studies focus particularly on the general competence of health sciences experts.In addition to external evaluations, studentsʼ self-evaluation regarding competencies is relevant to the development of educational policy and high-quality health care.

Background
The European Qualification Framework (EQF) (European Union (EU), 2017) defines competence levels in higher education as the knowledge, skills, and responsibilities that degree programmes should offer at the general level.For example, in Finland, health care professionals (registered nurses etc.) must complete an EQF level 6 qualification in universities of applied sciences.Health sciences bachelor's (EQF 6) and master's (EQF 7) degrees educates future health science experts (Keystone, 2023) who can develop health care, thereby leading and implementing the evidence-based practice (Jordan et al., 2019).In Finland, a bachelor's degree in health sciences is equivalent to a health care professionals' degree, but the students do not obtain a professional qualification.In this study, health science bachelor's and master's students represent degree programmes from nursing science, health management, public health, gerontology, health science education, health promotion, sports medicine, physiotherapy, and nutrition.Such students may also have social or health care professionals' qualifications from a university of applied sciences.
Future health sciences experts require a wide range of generic competence.The Organisation for Economic Co-operation and Development (OECD) (2018) describes general competencies to include disciplinary and interdisciplinary skills in thinking creatively as well as considering attitudes and values at personal and societal levels.Tuononen et al. (2022) defined generic competencies of higher education students to include professional skills, analytical skills, communication, collaboration, ethics, research, and career skills.Generic competencies are essential for students for employability and have been indicated to be better reflected in universities' strategies and curriculums of degree programmes (Chan et al., 2017;Tuononen et al., 2019).Overall, difficulties have been pointed out by measuring generic competence due to various definitions (Chan et al., 2017;Tuononen et al., 2022).There are a few instruments that have been developed to measure core competence among health care professionals (Al Jabri et al., 2021;Flinkman et al., 2017;Tuononen et al., 2022).Pramila-Savukoski et al. (2024) developed the health sciences generic competence self-evaluation instrument (HealthGenericCom) to measure generic health sciences competence, which is applicable to social and health care experts.
Generic competence in health sciences includes specific areas as indicated in previous literature (Table 1).Generic competence includes competence in leadership, administration, and financial skills, which entail promoting the change in managing social and health care services, financial aspects and leading teams (Al Jabri et al., 2021;Heinen et al., 2019).Health sciences experts should be capable of developing social and health care systems in a people-centric manner (Al Jabri et al., 2021;Kakemam et al., 2020;Koskenvuori et al., 2019;Pramila-Savukoski et al., 2022) and promote the health and well-being of the population (Pramila-Savukoski et al., 2022;WHO, 2022).This requires having competence in evidence-based healthcare, which is explained by the ability to identify current global health knowledge needs, generate and synthesise the evidence, and transfer and implement the evidence into health care by facilitating it in the workplaces (Al Jabri et al., 2021;  Jordan et al., 2019).Since health care is undergoing a major digitalization phase, digital competence is an essential area.As an expert, one should have a thorough understanding of developing people-centred digital services (Brice and Almond, 2020;EU, 2022;Nazeha et al., 2020;Pramila-Savukoski et al., 2022;Strudwick et al., 2019;WHO, 2021).One should take care of own well-being and manage own activities, set goals and prioritise (OECD, 2018;Maeda and Socha-Dietrich, 2021).In addition to these competence areas, development should be done in collaboration with multidisciplinary teams by sharing knowledge (Al Jabri et al., 2021;Maeda and Socha-Dietrich, 2021;Pramila-Savukoski et al., 2022).Health sciences experts should be part of different kinds of national and international networks (Skarbaliene et al., 2019;EU, 2017).
Previous studies have revealed that health sciences students realised that they needed to have know-how about collaboration, making ethical decisions, and leading others and themselves (Pramila-Savukoski et al., 2022).Sarkar et al. (2021) indicated the need for technology and digital competence among health science students.Ursin et al. (2021) examined bachelor students' general skills (e.g.collaboration and critical thinking skills) and revealed that over half of the students achieved a satisfactory or low level of such skills.Socioeconomic education explained the differences in the skill levels.In general, competencerelated studies in the social and health care context have focused mainly on specific areas of studentsʼ competence, such as the development of evidence-based practice skills (Ehrenberg et al., 2016;Ruzafa-Martínez et al., 2016) or people-centeredness (Rosewilliam et al., 2019).Additionally, it has been shown that pedagogical practices have enhanced the development of generic skills (Adriaensen et al., 2019;Tuononen et al., 2022;Virtanen and Tynjälä, 2019).To have highly competent experts, it is important to give attention to general competence levels in health sciences and factors relating to that.Thus, curriculums and learning methods could be more purposefully developed.Competency-based high-quality education is considered a solution to the challenges and labour shortages in the health system (WHO, 2022).

Study design
A cross-sectional observational study design was adopted in this study.The strengthening the reporting of observational studies in epidemiology (STROBE) checklist has been utilised to support the transparency of the study (Von Elm et al., 2007).

Aim
The aim of the study was to describe self-evaluated generic competence of health sciences students and the associated factors.
The following research questions were addressed in this study: 1. How do health sciences students self-assess their generic competence levels and how can these be clustered into profiles?2. Which factors are associated with the generic competence level in health sciences among health sciences students?

Participants
Bachelor's and/or master's students (n = 1400) from five universities in Finland were invited to participate in this study.No power estimation was conducted, since there are no previous studies on such a population.Therefore, all the health sciences students studying in five universities in Finland were invited to participate.The Cohen's d effect size was calculated to ensure sufficient power of the present sample (see Table 2).The inclusion criteria were that students were 1) presently studying health science for a bachelorʼs or masterʼs degree in one of the five universities from which we invited participation and 2) were willing to participate in the study.

Data collection
The data was collected during the spring of 2022 via a Webropol online survey.An invitation to participate in the survey was sent via email once, with three two-week reminders sent through the contact person of the universities for the students.

Data analysis
The data was analysed with IBM SPSS Statistics (V27.0,IBM Corporation, Armonk, NY).Socio-demographic data were analysed using descriptive statistics, percentages, means, and standard deviations.The data representing the eight sum variables were collected and clustered into generic competence profiles using the K-mean cluster classification (Rauf et al., 2012).The four cluster-model was determined to be the most optimal to represent health science generic competence profiles of health sciences' students (A, B, C, D).The relationship with the sociodemographic data and factors among the profiles were tested using the Chi-square test and one-way analysis of variance (ANOVA).Differences between four profiles were found with the Kruskal-Wallis and Mann-Whitney tests and were identified as being statistically significant when p < 0.05 (Munro, 2005).After we identified statistically significant differences among the four profiles, a Bonferroni correction was applied to evaluate how each profile differed significantly from the others.

Ethical considerations
Research permissions were collected from each participating organisation.According to Finnish regulations, ethical approval was not required because the study did not violate the integrity of the participants, the data were not used without informed consent, and the participants were not under 18 years of age (Medical Research Act 2010/ 794, 2010).Participants' privacy, humanity, and voluntariness was taken care of (Declaration of Helsinki, 2013;Finnish Research Integrity Advisory Board, 2019).Each participant also provided informed consent for their participation.Further, data has been protected according to the regulations of the Data Protection Act (1050/, 2018) and General Data Protection Act (2018).

Studentsʼ characteristics
A total of 291 students participated in the study (response rate 20.8 %).The mean age of the students was 34 years and the most of them (n = 260, 89.30 %) were female (see Table 2).Half of the participants (n = 149, 51.20 %) had a bachelor's degree from applied sciences as the highest degree.Over half of the participants were studying for a master's degree (n = 194, 66.70 %) and for the most part (n = 109, 37.80 %) had 61-120 ECTs conducted as part of their studies.Moreover, the majority of them had not previously participated in national conferences, continuing education, or research and development projects (n = 222, 76.30 %) and had not completed 5 ECTs in practical training in social and health care in their studies (n = 184, 63.20 %).Participants had 5-10 years of work experience in the social and health care sectors, at a position of a social and health care professional (n = 182, 62.50 %).

Studentsʼ competence profiles and associated factors
Four competence profiles (A = novice, 18 %; B = advanced, 23 %; C = skilled, 33 %; D = expert 26 %) were identified among health sciences students, with statistically significant differences among all but one competence areas (p < 0.001).All the competence areas were evaluated as being moderate to very good (mean min 2.29-max 4.38).Values <1.49 were reported as poor, 1.5-2.49as moderate, 2.5-3.49as good, 3.5-4.49as very good, and > 4.5 as excellent.Digital competence was evaluated as the lowest competence area (2.98, ±0.80), while competence in collaboration and problem-solving was evaluated as the highest (3.77, ±0.60).In digital competence, no statistical difference was not found between Profiles A and B (p = 0.448).The background factors, including age (p < 0.001), highest degree completed (p = 0.003), current degree level (p < 0.001), work-based practical training in social and health care (minimum 5 ECTs) (p = 0.048), and position in social and health care (p < 0.001) differed statistically significantly among the profiles.Profile A included those who exhibited moderate to good competence (mean min 2.30-mean max 3.12), Profile B included those who exhibited moderate to very good competence (mean min 2.29-mean max 3.63), Profile C included those who exhibited good to very good competence (mean min 3.01-mean max 3.83), and Profile D included those who exhibited very good competence in all eight areas (mean min 3.71 -mean max 4.38) (Table 2).Profile A included 53 participants (18.20 %), of which 92.50 % were female.They self-evaluated their competence in leadership, administration and finance (2.30, ±0.47), digital competence (2.39, ±0.62), and competence in societal interaction (2.40, ±0.42) as moderate and others as good.Further, participants in Profile A evaluated their digital competence as higher than that by Profile B. In Profile A, the respondents' average age was 31 years, which was lower than that in Profiles C and D. Profile A included almost half of students with bachelor's degree as their current degree (49.10 %) and least with master's degree (50.90 %).In total 37.30 % completed 0-60 ECTs in their studies.Most of the participants (71.70 %) had not previously participated in conferences, continuing education, and research or development projects.In addition, 22.60 % of the respondents had practical training in social and health care.The majority (94.30%) of the respondents had 5-10 years of work experience in the social and health sector and 56.60 % of the participants previously worked in professional jobs in the social and health care sectors.
Profile B included 68 participants (23.30%) of which 83.80 % were female.In Profile B, the respondents self-evaluated their digital competence as moderate 2.29 (±0.46), which was the lowest compared to other profiles.Competence in health promotion (mean 3.58, ±0.61), competence in work well-being and self-management (mean 3.60, ±0.47), and competence in collaboration and problem-solving (mean 3.60, ±0.42) were evaluated as very good, but lower than that by participants in Profiles C and D. Other competence areas were evaluated as good.After Bonferroni's correction, no statistically significant difference was not found between Profiles A and B (p = 0.448) in terms of digital competence.In Profile B, the average age was 31 years, which was the lowest compared to the other profiles.Half of the participants in Profile B had a bachelor's degree as their current degree (48.50 %).In total, 38.80 % had conducted 61-120 ECTs and had not previously participated in conferences, continuing education, and research and development projects (76.50 %).Work-based practical training in the social and health care sectors was completed by 32.40 % of the respondents.Further, the majority (92.50 %) of the respondents had 5-10 years of work experience in the social and health care sectors.A total of 54.40 % of the participants previously worked in a professional job in the social and health care sectors and had more respondents who had no experience in healthcare positions (27 %) when compared to other profiles.
Profile C included 95 participants (32.60 %) of which 90.50 % were female (average age was 34 years).In Profile C, the respondents selfevaluated competence in leadership, administration and finance (mean 3.19, ±0.48), digital competence (mean 3.23, ±0.52), and competence in societal interaction (mean 3.01, ±0.43) as good and other competence areas as very good.They had mostly a master's degree as their current degree (75.80 %).Profile C included 29.50 % of participants with 61-120 ECTs conducted and more participants with over 180 ECTs conducted (18.90 %) as compared to other profiles.A total of 70.50 % had not participated in national conferences, continuing education, and research and development projects.In total, 43.20 % had completed work-based practical training in the social and health care sector, which was the highest proportion among all the profiles.The respondents had work experience of 5-10 years in the social and health care sectors (89.40 %).In addition, they had more experience of being engaged in a professional job in the social and health care sectors (71.60 %) as compared to other profiles.
Profile D included 75 participants (25.70 %) of which 90.70 % were female.The respondents evaluated their competence in every competence area as very good.Competence in health promotion (mean 4.29, ±0.44) and competence in collaboration and problem-solving (mean 4.38, ±0.36) were evaluated as the highest.The respondents had an average age of 38 years, which was higher than that in other profiles.In Profile D, the respondents had more students with a master's degree as the current degree (80.00 %) than those in other profiles.Moreover, half of the respondents had 61-120 ECTs conducted (50.70 %).The majority (86.70 %) had not participated in national conferences, continuing education, or research and development projects.Further, 42.70 % of the respondents had completed practical training in the social and health care sectors.The majority had 5-10 years' work experience (93.30%) in a professional job (62.70 %).Profile D included participants who had experiences in various other professional jobs or expert positions (25.30%), which was higher than that of participants in other profiles.

Discussion
The aim of this study was to describe self-evaluated generic competence in health sciences among health science students and associated factors.No competences were rated as excellent in any of the profiles.When comparing the results with Ursin et al. (2021), our study demonstrated higher competence among health sciences students.Ursin et al. (2021) reported that approximately 40 % had generic skills that were deemed to be at a good level of competence.However, Ursin et al. (2021) used task-based evaluation and the participants in their study were younger than those in our study.We indicated that health sciences students were most competent when older and studying for a masterʼs degree.
Competence in collaboration and problem-solving was rated good or very good and it was evaluated as the highest in Profiles B, C, and D and the second highest in Profile A. The collaboration competence of health science experts is important in solving complex problems together (Pramila-Savukoski et al., 2022) which is crucial for higher productivity and person-centred care (Maeda and Socha-Dietrich, 2021).Learning environments and teaching (with collaborative methods) play a role in the learning of generic competences (Räisänen et al., 2022).In contrast, Tuononen et al. (2019) longitudinal study revealed that collaboration skills were developed the least among university students.
Competence in health promotion was evaluated at least as good in all the profiles.In Profiles A and C, competence was evaluated as the highest, in B as the third highest, and in D the second highest.Health sciences students must be able to define the concept and meaning of health and well-being for their clients and promote the processes related to social and health care (Pramila-Savukoski et al., 2022;WHO, 2022).As a health science expert, one should be able to identify the health promotion needs of the population and develop health promotion methods, practices, management, and education.
Digital competence consists of basic information technology skills, information management, digital communication, ethics, legal issues, and data protection and security (Brice and Almond, 2020;Jarva et al., 2023;Nazeha et al., 2020).In all profiles, digital competence was rated as one of the lower skills areas.Low competence in digital and technological aspects among students has also been indicated by Sarkar et al. (2021) in analysing data and using technology.Health sciences students aim to possess digital skills and, in general, value digital competence as an expert (Pramila-Savukoski et al., 2022).Nursing managers need to be aware of the development in information technology, as they lead to the development of digital solutions in their organisations (Heinen et al., 2019;Strudwick et al., 2019).Nevertheless, it is obvious that digitalisation and artificial intelligence shape our vision of social and health care and cause the uncertainty and challenges that may be evident in self-evaluations.
In Profile A, the lowest competence level was measured in competence in leadership and administration and finance, whereas in Profiles B and C, it is the second lowest.Competence in management and leadership is important for health sciences experts because they manage not only teams but also projects and their finances (Heinen et al., 2019).Understanding the change, its implications for the work community and service renewal is an important part of management and leadership skills (Heinen et al., 2019;Le-Dao et al., 2020).In Profile A, slightly more than half of the respondents had worked in professional jobs in the social and health sectors and half of them had a bachelorʼs degree as their current level of education.If the respondent had experiences with various professional jobs and expert positions, competence in leadership and administration and finance was higher.There were only a few participants in Profiles C and D who reported holding management expert positions or project leader experiences.Further, although health management and leadership skills have been successfully developed through various methods and training (Ayeleke et al., 2019), it appears that this competence should be included more in studies at the bachelor level as well.
Competence in societal interaction was relatively low, particularly in Profile A, where it was the third lowest, and in Profiles B, C, and D, where it was the second lowest.The capability of working in different kinds of networks and possessing various communication skills are described in EQF (EU, 2017).Health sciences experts should be able to participate in social debate (Skarbaliene et al., 2019).Thus, it is important to prepare students for communication skills during their education.As a health sciences expert, it is essential to follow and evaluate the global changes that affect policies at the national level (Jordan et al., 2019;Pramila-Savukoski et al., 2022).
In our study, the background factors of age, highest degree completed, current degree level, work-based practical training in social and health care (minimum 5 ECTs), and position in work experience in the social and health care sectors differed statistically significantly among the profiles.In the study of Ursin et al. (2021) university students' previous educational background was associated with the level of generic competence and university students had higher levels of generic skills than students from universities of applied sciences.Interestingly, in our study, work experience was not statistically significant among participants in any of the profiles, unless there were few participants with under 5 years' experience in profile D. Neither was participating in conferences or other continuous education.Work-based learning has been shown to be effective in developing competence (Mianda and Voce, 2018).Profile A included a few participants who had conducted practical training in social and health care sectors as compared to participants other profiles.
In Profile A, participants had conducted fewer ECTs than students in other profiles.Nevertheless, the difference in conducting ECTs between profiles was not statistically significant.This may indicate that students have the possibility to learn competence during studies with different kinds of learning methods.Attention must be paid to the learning of generic skills so that higher education graduates have the skills to meet the needs of the working life (Chan et al., 2017).For example, Tuononen et al. (2019) have reported a wide range of skills and the ability to recognise them at graduation is important and can also be linked to the challenges graduates face in the working life.It is important to pay attention to education/degrees to bridge the gap between working life and education (Fan et al., 2015;Skarbaliene et al., 2019;Räisänen et al., 2022).Moreover, it is also worth noting that more than half of the total number of participants in this study had not engaged in work-based training.The generic competence in health sciences was lower in Profiles A and B, in which half of the respondents were bachelor's degree students.Understanding the generic competencies in health sciences is essential to provide high-quality social and health care and helps to focus more on low-evaluated competence areas such as digital competence and leadership, administration and finance, or designing health science education, particularly work-based training and project management.

Limitations and strengths
This study has a few limitations.First, the response rate was 20 % and a larger national sample would have provided more precise outcomes.However, this data has been shown to be reliable.Second, these findings represent only health science students in one European country.Third, almost 90 % of the respondents were female.A larger and more diverse sample would provide more generalisable results.To ensure a sufficient effect size of the present sample, Cohen's d values were calculated to compare the profiles.The results can be interpreted in the following manner: values <0.2 represent a small effect size, values 0.21-0.50represent a moderate effect size, values 0.51-0.80represent a large effect size, and values 0.81-1.30represent a rather large effect size (Cohen, 1992).Cohen's d denoted small to very large in one sum variable, medium to very large in three sum variables, and large to very large in three sum variables among the profiles.One sum variable had a rather large effect size among the profiles (0.93-3.82) (see Table 2).

Conclusion
Generic competencies in health sciences were rated as moderate to very good.No area was evaluated as excellent.When evaluating the total sample, digital competence was rated the lowest, which is very important to consider in the development of education and cooperation with stakeholders.We recommend that more attention should be paid to teaching/learning methods and supporting young people in developing their generic competence and work-based learning.The students should have more opportunities to develop their skills through a variety of responsibilities and leadership roles; it is necessary to develop and implement work-based training.The results of this study can also be utilised in competence management and to lead experts.In addition, mentoring and knowledge-sharing can be included in education to create strong competence in students.Generic competencies must be discussed in greater detail in future research.

Table 1
Competence areas in health sciences.

Table 2
Health science' students' (n = 291) sociodemographic backgrounds and health sciences generic competence profiles.continuingeducation,researchordevelopingprojects; work-based practical training in the social and health care sectors (minimum of 5 ECTS), work experience in social and health care in terms of number of years, and the position in which the participant has worked in social and health care services (Table2).The content and construct validity of the instrument were reported in Pramila-Savukoski et al. (2024) study.In this study, the internal consistency of the instrument was acceptable based on Cronbach's alpha values between 0.85 and 0.95 (Von Elm

Table 2
(continued ) c One way ANOVA.d Bonferroni correction.e Chi-square test.f Fisher exact test.g Kruskal Wallis test.h Mann-Whitney test.S. Pramila-Savukoski et al.