Identifying and assessing expertise gaps in healthcare education across Europe: a cross-sectional study 

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Although previous studies have identified significant variations in the availability of educational resources, access to specialized training, and distribution of qualified educators, none have identified practical solutions to address these gaps. This study aimed at developing and validating a tool for identifying expertise gaps in healthcare education and assessing these gaps across Europe. Methods A cross-sectional survey was conducted between April-September 2024 involving healthcare professionals and academics. The survey, developed by a multidisciplinary team within an EU-funded project, explored expertise gaps in hard, transversal, digital, and green skills in the domains of: ‘knowledge’, ‘practice’, ‘research’, and ‘teaching methods’. Face validity, reliability and construct validity were assessed using Cronbach's alpha and confirmatory factor analysis (CFA). Results A total of 230 responses were collected. Gaps in digital and green skills were identified across all domains. Research-related gaps were related to methodological knowledge and skills. Gaps in teaching methods included lack of innovative and student-centred approaches. The tool demonstrated strong internal consistency (Cronbach’s α ≥ 0.80), and Confirmatory Factor Analysis supported its construct validity. Conclusions This validated tool can be used to map academic expertise gaps in healthcare education at local, national and international levels. Targeted interventions in digital and green competencies, innovative pedagogy, and research training are needed. The tool offered a practical starting point for identifying skill gaps, aligning with the United Nations Sustainable Development Goals and Europe’s commitment to high-quality, inclusive education. Expertise gaps healthcare education instrument validation cross-sectional study factor analysis Figures Figure 1 Background The healthcare sector across Europe faces persistent disparities in expertise, impacting on the quality of education and patient care [ 1 ]. These inequities involve all healthcare disciplines. Projects like SAFE EUROPE have highlighted significant variations in healthcare education across Europe, particularly in smaller and low-income countries [ 2 ]. In line with the United Nations Sustainable Development Goal 4 ‘Ensuring inclusive and equitable quality education’ [ 3 ], the SAFE EUROPE project highlighted insufficient access to specialised expertise and practical training in some countries [ 4 ]. This leads to disparities in the expertise of healthcare professionals, impacting on the quality and equity of patient care [ 3 , 5 ]. Expertise gaps have been reported for several healthcare professions receiving less comprehensive training, including radiotherapy educators who often lack peer support and struggle to stay abreast with technological advances [ 6 ]; nursing programmes facing shortages of qualified faculty [ 7 ]; nutritionists and dietitians encountering educational barriers that limit their contribution to sustainable food systems [ 8 ]. These issues are compounded by outdated curricula, insufficient funding, and limited access to equipment and clinical training. Small academic teams may lack the expertise to cover all areas, limiting student exposure. Brain drain further worsens this situation [ 9 ]. Skill gaps increasingly include transversal and green competencies. Green skills involve values and knowledge that support sustainable healthcare, while transversal skills such as communication, teamwork, and critical thinking are essential for collaborative practice [ 10 ]. Despite growing awareness of these needs, studies that systematically map expertise gaps across knowledge, practice, research, and teaching in a multi-professional European context are lacking. Such mapping is crucial to inform targeted educational strategies. Methods Due to the absence of a single model that comprehensively addressed skill gaps in the domains of knowledge, practice, research, and teaching simultaneously, the survey development process began with brainstorming among our multidisciplinary research team members. To guide the brainstorming session, various existing frameworks such as Bloom’s Taxonomy [ 11 ] (for knowledge and teaching), the Dreyfus Model of Skill Acquisition [ 12 ] (for practice and teaching), Boyer’s Model of Scholarship [ 13 ] (for research and teaching), and the Tuning Project [ 14 ] (for skills and knowledge gaps between academic offerings and workplace demands) were considered. Four key skill categories were also identified: hard, transversal, digital, and green, to be considered within the domains of knowledge, practice, research, and teaching. Based on this integrated approach and informed by current challenges and previous projects on expertise gaps [ 6 , 15 – 17 ], we collaboratively developed and refined the survey questions (Supplementary File 1). Aim To identify expertise gaps in knowledge, practice, research, and teaching methods in healthcare professional education across Europe. A secondary objective was to develop and validate a survey that effectively measured expertise gaps in healthcare professionals. Design This study employed a cross-sectional design using an online survey methodology. Informed by a pragmatist research philosophy, the study focused on practical, context-sensitive insights into healthcare education across Europe. Clinical trial number Not applicable. Ethics statement Ethical approval was obtained from the local Research Ethics Committee. All participants provided informed consent before accessing the survey. The survey was anonymous, with no identifiable data collected, and procedures complied with the Declaration of Helsinki. Participants Eligible participants were healthcare professionals and educators involved in teaching or clinical training in Europe, including physicians, nurses, physiotherapists, medical physicists, radiographers, and public health professionals. Open recruitment used a non-probabilistic sampling strategy. Survey links were disseminated via email and social media. Data were collected anonymously through Easy Feedback ( https://easy-feedback.com/ ) from July to September 2024. There were no exclusion criteria to facilitate a broad multidisciplinary representation. Variables Primary outcome variables were self-reported expertise gaps in four academic domains: Knowledge, Practice, Research, and Teaching Methods. Within each domain, gaps were assessed across Hard, Transversal, Digital, and Green skills, using a 10-point Likert scale (10 = greater gap). At the end of the questionnaire, there was an open-ended question where participants could briefly describe any additional expertise gaps, enabling to collect some descriptive qualitative data. (Supplementary File 1) The validation process Content validity was assessed by eight experts using a 4-point Likert scale. Item-level (I-CVI) and scale-level (S-CVI) indices were calculated. Face validity was evaluated by ten professionals who reviewed the clarity and comprehensibility. The final survey had five sections: Demographics, Knowledge, Practice, Research, and Teaching Methods. Participants could also suggest additional gaps. Reliability and construct validity Reliability was assessed using Cronbach’s alpha (≥ 0.80 threshold). Confirmatory Factor Analysis (CFA) evaluated the instrument’s construct validity, confirming the alignment between theoretical constructs and survey items. Study sample size No a priori sample size was calculated. A total of 230 responses were deemed adequate for tool validation and CFA, following the 5–10 participants-per-item guideline. Post hoc analysis showed good internal consistency and model fit. Statistical methods Correlation analysis examined links between demographics and perceived gaps. Pearson’s or Spearman's coefficients were used depending on variable type. Logistic regression explored predictors of perceived gaps. Qualitative responses were analysed to identify themes. All analyses were conducted using Jamovi (Version 2.6). Results Participants A total of 230 participants responded to the survey, of which 67% women (n = 153) and 33% men (n = 77). Due to the open distribution strategy via social media and professional networks, the total number of individuals reached could not be determined, making it impossible to calculate a response rate. The average age was 45.8 years (SD = 12.5), the average length of work experience was 20.5 years (SD = 11.9), while the average time in the current role was 12.4 years (SD = 8.83). The main geographical areas included Eastern Europe, Northern Europe, Southern Europe, and Western Europe. Various professional categories were represented, including medical doctors (15.2%, n = 35), nurses (13.9%, n = 32), allied health professionals (57.0%, n = 131), academics (7.0%, n = 16) and ‘others’ (7.0%, n = 16). A total of 53 (23%) participants replied to the open-ended question at the end of the questionnaire with a brief description of any additional gaps. [INSERT Table 1 HERE] Table 1 Sample characteristics Geographical area Profession N (%) Mean (SD) Mean (SD) Mean (SD) N (%) N (%) Age in years Years of work experience Years in current role Female Male Total 230 45.8 (12.5) 20.5 (11.9) 12.4 (8.83) 153 (67%) 77 (33%) Eastern Europe 6 (2.6%) 51.5 (6.47) 27.0 (4.73) 20.2 (3.54) 4 (1.7%) 2 (0.9%) Medical doctors 2 48.0 (2.83) 25.5 (0.71) 22.5 (4.95) 1 (0.4%) 1 (0.4%) Nurses 0 - - - 0 0 Allied health professionals 2 47.5 (0.71) 23.0 (1.41) 20.5 (0.71) 2 (0.9%) 0 Other 1 55.0 35.0 15.0 0 1 (0.4%) Academic 1 63.0 30.0 20.0 1 (0.4%) 0 Northern Europe 37 (16.1%) 53.9 (10.5) 27.0 (11.7) 13.4 (11.7) 28 (12.2%) 9 (3.9%) Medical doctors 5 54.2 (12.3) 22.6 (15.8) 14.2 (17.9) 3 (1.3%) 2 (0.9%) Nurses 8 53.5 (9.46) 27.4 (10.7) 11.5 (9.09) 7 (3.0%) 1 (0.4%) Allied health professionals 20 54.5 (10.6) 28.4 (10.6) 14.3 (11.4) 17 (7.4%) 3 (1.3%) Other 2 49.5 (20.5) 27.5 (20.5) 3.00 (1.41) 1 (0.4%) 1 (0.4%) Academic 2 52.5 (12.0) 22.0 (18.4) 21.0 (15.6) 0 2 (0.9%) Southern Europe 142 (61.7%) 42.8 (11.2) 18.7 (11.2) 12.2 (7.85) 97 (42.2%) 45 (19.6%) Medical doctors 23 43.3 (12.3) 16.1 (13.2) 13.4 (9.26) 14 (6.1%) 9 (3.9%) Nurses 18 47.2 (9.67) 25.2 (9.61) 12.8 (7.53) 11 (4.8%) 7 (3.0%) Allied health professionals 86 41.0 (11.1) 17.4 (10.7) 11.7 (7.27) 60 (26.1%) 26 (11.3%) Other 9 44.6 (10.5) 21.3 (11.4) 9.89 (10.3) 7 (3.0%) 2 (0.9%) Academic 6 52.3 (6.86) 25.0 (6.07) 16.8 (7.08) 5 (2.2%) 1 (0.4%) Western Europe 27 (11.7%) 43.4 (13.4) 16.7 (11.8) 9.22 (7.98) 14 (6.1%) 12 (5.2%) Medical doctors 2 33.0 (1.41) 10.5 (0.71) 7.00 (4.24) 14 (6.1%) 1 (0.4%) Nurses 3 54.3 (13.4) 19.7 (15.6) 10.7 (7.57) 11 (4.8%) 1 (0.4%) Allied health professionals 17 42.4 (14.0) 17.2 (13.2) 9.41 (8.81) 60 (26.1%) 6 (2.6%) Other 2 34.5 (4.95) 12.0 (11.3) 7.50 (9.19) 7 (3.0%) 2 (0.9%) Academic 3 51.3 (10.8) 18.0 (5.29) 9.33 (9.29) 5 (2.2%) 3 (1.3%) Other 14 (6.1%) 55.1 (14.3) 23.9 (13.5) 14.7 (11.5) 7 (3.0%) 7 (3.0%) Medical doctors 2 57.5 (6.36) 20.0 (19.8) 21.5 (12.0) 14 (6.1%) 1 (0.4%) Nurses 2 67.0 (9.90) 45.0 (7.07) 28.5 (19.1) 2 (0.9%) 0 Allied health professionals 5 40.8 (13.1) 14.4 (11.1) 9.00 (6.44) 4 (1.7%) 1 (0.4%) Other 2 68.5 (3.54) 28.5 (2.12) 18.0 (12.7) 0 2 (0.9%) Academic 3 60.7 (5.03) 25.0 (3.00) 8.33 (6.81) 0 3 (1.3%) North America 4 (1.7%) 50.3 (20.1) 27.0 (11.5) 9.50 (2.38) 3 (1.5%) 1 (0.4%) Medical doctors 1 63.0 29.0 11.0 1 (0.4%) 0 Nurses 1 42.0 22.0 8.00 0 0 Allied health professionals 1 70.0 42.0 12.0 0 0 Other 0 - - - 1 (0.4%) 0 Academic 1 26.0 15.0 7.00 0 1 (0.4%) Reliability and Construct Validity Analysis The survey demonstrated strong internal consistency across all domains: Knowledge (α = 0.877), Practice (α = 0.920), Research (α = 0.929), and Teaching (α = 0.960). [INSERT Table 2 HERE] Table 2 Reliability Analysis Internal Consistency Scale Reliability Statistics Scales Cronbach's α Knowledge 0.877 Practice 0.920 Research 0.929 Teaching 0.960 0.866 CFA confirmed construct validity, whereby all factor loadings were significant (p < 0.001), indicating robust psychometric properties. [INSERT Table 3 HERE] Table 3 Confirmatory Factor Analysis Factor Loadings Factor Indicator Estimate SE Z p KNOWLEDGE A1 1.59 0.134 11.87 < .001 A2.1 1.18 0.200 5.90 < .001 A2.2 1.47 0.171 8.64 < .001 A2.3 1.77 0.157 11.30 < .001 A2.4 1.80 0.175 10.32 < .001 A2.5 1.60 0.205 7.80 < .001 A4 1.73 0.129 13.45 < .001 A5 1.44 0.135 10.71 < .001 A6 1.30 0.133 9.74 < .001 A7 1.38 0.148 9.33 < .001 PRACTICE B1 1.74 0.128 13.51 < .001 B2.1 1.26 0.219 5.75 < .001 B2.2 1.56 0.189 8.22 < .001 B2.3 2.01 0.142 14.22 < .001 B2.4 1.71 0.172 9.98 < .001 B2.5 1.66 0.199 8.34 < .001 B4 1.88 0.125 15.02 < .001 B5 1.59 0.127 12.51 < .001 B6 1.36 0.142 9.54 < .001 B7 1.28 0.148 8.69 < .001 RESEARCH C1 1.99 0.136 14.64 < .001 C2.1 1.54 0.203 7.58 < .001 C2.2 1.78 0.175 10.17 < .001 C2.3 1.91 0.149 12.84 < .001 C2.4 1.90 0.155 12.22 < .001 C2.5 1.72 0.193 8.88 < .001 C4 1.97 0.132 14.89 < .001 C5 2.03 0.124 16.46 < .001 C6 2.02 0.132 15.36 < .001 C7 1.85 0.140 13.15 < .001 TEACHING METHODS D1 1.90 0.122 15.54 < .001 D2.1 1.45 0.205 7.10 < .001 D2.2 1.70 0.173 9.84 < .001 D2.3 2.03 0.127 16.00 < .001 D2.4 2.05 0.141 14.52 < .001 D2.5 1.91 0.162 11.79 < .001 D4 2.03 0.128 15.90 < .001 D5 1.87 0.119 15.68 < .001 D6 1.66 0.134 12.32 < .001 D7 1.64 0.146 11.22 < .001 [INSERT FIGURE 1 HERE] Therefore, this tool was considered reliable and validated to measure expertise gaps of healthcare professionals and was used to assess these gaps across Europe. Expertise gaps of healthcare professionals across Europe Expertise Gaps in Knowledge Digital (mean: 5.04) and green skills (mean: 6.16) constituted the most critical gaps across all regions. Additional gaps were noted in emerging medical fields (e.g., AI, radiobiology) and in transversal competencies, such as leadership and critical thinking. (Supplementary File 2) Through the open-ended question, participants highlighted gaps in specialised areas such as advanced medical imaging, radiobiology, prosthodontics, molecular pathology, and AI in radiology, indicating the need for greater expertise in emerging technologies. Gaps in transversal skills—including leadership, communication, and critical thinking—were also reported. In occupational therapy and nursing, knowledge gaps were reported in community-based care, geriatrics, paediatrics, and digital health literacy. Broader systemic challenges, including outdated curricula, limited post-graduate programmes, and misalignment between theory and clinical practice, further underscored the need for targeted, interdisciplinary, and technologically informed education across healthcare professions and regions. Expertise Gaps in Practice Practice gaps reflected those in knowledge, where green (mean: 5.96) and digital skills (mean: 5.03) again scored highest. Respondents reported insufficient clinical training, limited exposure to technology, and underdeveloped interprofessional collaboration. (Supplementary File 3) Through the open-ended question, participants highlighted difficulties translating theory into clinical practice, due to limited hands-on training, inadequate facilities, and scarce exposure to advanced techniques. There was also a need to integrate digital health, AI, and sustainability into care delivery processes. Challenges also included poor interprofessional collaboration, cultural competence, and gaps in leadership and decision-making. These findings underline the importance of clinical supervision, experiential learning, and updated training to bridge the theory–practice divide. Expertise Gaps in Research Main gaps were reported in research methods, especially among respondents with postgraduate education. Issues included lack of training, limited access to resources, and competing clinical responsibilities. Green (mean: 6.27) and digital skills (mean: 5.59) scored highest. (Supplementary File 4) Participants reported widespread gaps in research skills, including methodology, critical appraisal, and statistics. Barriers included limited funding, mentorship, lab access, and complex procedures for ethics approval. Emerging fields such as AI, sustainability, and disaster preparedness were underrepresented, alongside gaps in public and mental health research. Limited postgraduate training and overreliance on qualitative approaches, especially in nursing, further reduced research capacity. Expertise Gaps in Teaching Methods Teaching methods showed gaps in green (mean: 6.05) and digital skills (mean: 5.33) (Supplementary File 5). Outdated pedagogies, insufficient use of digital tools, and inadequate training in innovative teaching approaches (e.g., simulation, flipped classrooms) were frequently mentioned. Participants reported gaps related to outdated teaching approaches, with limited use of innovative methods such as flipped classrooms, simulation, and digital tools. Many educators lacked formal pedagogical training and exposure to student-centred strategies. Key barriers included scarce resources, weak links between academic and clinical teaching, and underrepresentation of emerging topics like AI, digital health, and multicultural competencies. More interactive, experiential learning was recommended. Other expertise gaps Additional challenges included a lack of recognition for certain professions (e.g., occupational therapists, and orthotics and prosthetics), inadequate CPD frameworks, and a disconnect between research and practice. Participants also identified structural issues in educational design and delivery. Correlation, Comparative, and Regression Models No significant correlations or group differences were found through regression, ANOVA, or correlation tests. This suggests widespread agreement across roles, regions, and demographic variables on the nature of the expertise gaps. Discussion We aimed to identify expertise gaps in healthcare education across Europe and validate a tool for mapping these gaps. The findings confirmed widespread gaps across four domains—knowledge, practice, research, and teaching methods—particularly in digital and green skills. The new tool demonstrated strong reliability and validity in assessing these dimensions. Interpretation and implications for practice Overall, we found compelling evidence of persistent and systemic challenges in healthcare professional education across Europe. The main expertise gaps regarded digital (mean: 5.04) and green skills (mean: 6.16), reflecting the increasing integration of technology in clinical practice and the imperative for environmentally sustainable healthcare [ 10 ]. These gaps were observed not only in knowledge but also in practice, teaching, and research, underscoring the pervasive nature of these needs. Transversal competencies—such as leadership, communication, adaptability, and critical thinking—were also underdeveloped (mean: 4.76), aligning with prior studies that emphasized the need for broader skill sets in modern healthcare [ 5 ]. The identified gaps reflect the difficulty of embedding these competencies within traditional curricula, especially in under-resourced institutions, where clinical and academic staff often lack pedagogical support or time to innovate [ 7 ]. Difficulties translating knowledge into practice were very pronounced. Practice gaps were often linked to insufficient clinical exposure and hands-on training, which are essential for bridging theory-practice gap [ 6 , 18 ]. These findings reinforce the importance of experiential learning and simulation-based methods as pedagogical strategies for developing skills and confidence among students and early-career professionals. Respondents from Eastern Europe reported more widespread gaps, especially at master's level education. While this finding aligns with prior research indicating disparities in education quality across European regions [ 5 ], the small size of this subgroup and lack of statistical significance call for cautious interpretation. The limited integration of emerging technologies such as artificial intelligence (AI), digital health, and green innovations into curricula and practice highlighted a lag between healthcare innovation and education. Similar patterns have been observed in literature, where AI and sustainability are often neglected in healthcare training despite their growing importance [ 15 ]. Finally, the lack of association between socio-demographic variables (i.e. age, role, experience) and perceived gaps suggested that these issues are pervasive and shared across professions and regions, further underscoring the systemic nature of our findings. Comparison with previous studies Our findings are consistent with the SAFE EUROPE project, which identified substantial variations in healthcare education across Europe, especially in smaller or low-income countries [ 2 ], with widespread presence educational inequalities, especially regarding green and digital skills. Other studies have highlighted the shortage of faculty with advanced degrees in nursing and radiotherapy [ 6 , 7 ]. Our study extended those insights by showing that the problem affects also related fields such as occupational therapy, dietetics, and medical imaging. The alignment with the CanMEDS framework [ 19 ], which emphasizes roles such as communicator, collaborator, and leader, also reinforces the call for curricula that include not only technical but also interpersonal and leadership skills. Furthermore, earlier research has highlighted limitations in existing pedagogical strategies, such as overreliance on traditional lectures and few educators prepared in innovative teaching methods [ 8 ]. These shortcomings were confirmed in our study, especially regarding limited use of simulation, flipped classrooms, and digital tools. Limitations The sampling strategy relied on open online dissemination, resulting in a self-selected sample. This approach may have led to response, as those with more interest in the topic were more likely to participate. To address this, the survey was widely shared across diverse professional and geographic networks. Anonymity helped reduce social desirability bias, although self-reporting bias remained a limitation. Furthermore, since the survey was distributed via professional networks and social media, the total number of individuals reached was unknown, making it impossible to calculate the response rate. The small sample size in some geographic subgroups limited the strength of comparative analyses across regions or professions. Another limitation was the use of self-reported data, which is inherently subjective and may not always reflect actual expertise levels. Despite these limitations, the study provides valuable insights across healthcare professions and European regions. These methodological decisions are consistent with a pragmatist approach to research, which values contextualised, actionable findings over methodological idealism [ 18 ]. Generalizability While our findings are not statistically generalisable to all European healthcare professionals, the broad representation of professions and regions supports their relevance and usefulness. The consistency of responses across demographic and geographic groups enhances the external validity of the results. This survey may also be applicable to other international settings facing similar challenges. Conclusions This study highlighted the need to address gaps in knowledge, practice, research, and teaching methods within healthcare education. Digital and green skills emerged as key areas requiring curricular and pedagogical innovations. Although this was an initial step, this study yielded useful insights to inform educational planning and support more equitable, future-ready learning environments. Further research is recommended to gain a deeper understanding of the barriers and opportunities related to bridging expertise gaps. Additionally, longitudinal studies could explore whether interventions such as digital platforms or educator training programs can effectively reduce gaps over time. In addition, based on our results, educational institutions are encouraged to consider incorporating digital and green competencies more systematically, alongside pedagogical training for faculty and opportunities for simulation-based and interprofessional learning. The ECHOES platform ( www.echoesplatform.eu ) holds the potential to support these efforts by promoting access to specialised expertise and fostering collaborative, cross-regional educational opportunities. By encouraging dialogue and expertise sharing among healthcare educators and professionals, the platform could contribute to a more inclusive and responsive European educational landscape. Abbreviations AI – Artificial Intelligence ANOVA - Analysis of Variance CFA – Confirmatory Factor Analysis CPD – Continuing Professional Development ECHOES - ExChange Of ExpertiSe in healthcare professionals’ education EU – European Union I-CVI – Item level Content Validity Index S-CVI – Scale level Content Validity Index SD – Standard Deviation Declarations Acknowledgements We thank the European Union for co-funding this study through the Erasmus+ ECHOES Project. Authors contributions Catherine Fitzgerald, Nicola Pagnucci, and Giuseppe Aleo contributed to the conception and design of the study, the methodology, acquisition of data, analysis and interpretation of data, validation, drafting the paper and final approval of the version to be published. Niamh Walsh contributed to the conception, and design of the study, the methodology, acquisition of data, and the drafting of the paper. Marjorie Bonello, Petra Jones and Rachael Agius contributed to the conception and design of the study, the methodology, and the final review of the manuscript. José Guilherme Couto contributed to the conception and design of the study, project administration, funding acquisition, the methodology, supervision and final review of the manuscript. Funding This study was co-funded by the European Union through the Erasmus+ Programme, within the framework of the project “ExChange Of ExpertiSe in healthcare professionals’ education” (ECHOES) [Project number: 2023-1-MT01-KA220-HED-000156744]. Disclaimer: Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or European Union Programmes Agency (EUPA). Neither the European Union nor the granting authority can be held responsible for them. Data availability All data generated or analysed during this study are included in this published article. However, any further information is available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study received ethical approval from the Research Ethics Committee of the Faculty of Health Sciences at the University of Malta (Reg. N. FHS-2024-00049). All participants provided informed consent before accessing the survey. The survey was anonymous, with no identifiable data collected, and procedures complied with the Declaration of Helsinki. Consent for publication Not applicable Competing interests The authors declare no competing interests References Abimbola S, van de Kamp J, Lariat J, Rathod L, Klipstein-Grobusch K, van der Graaf R, et al. Unfair knowledge practices in global health: a realist synthesis. Health Policy Plan. 2024;39(6):636–50. 10.1093/heapol/czae030 . McFadden S, Couto G, McClure P, Hughes C, Beardmore C. The SAFE EUROPE project: What is it all about? Radiography. 2022;28(3):874–5. 10.1016/j.radi.2022.06.013 . 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An evaluation of knowledge of circular economy among Therapeutic Radiographers/Radiation Therapists (TR/RTTs): Results of a European survey to inform curriculum design. Radiography. 2023;29(2):274–83. 10.1016/j.radi.2022.12.006 . Nagel DA, Penner JL, Halas G, Philip MT, Cooke CA. Exploring experiential learning within interprofessional practice education initiatives for pre-licensure healthcare students: a scoping review. BMC Med Educ. 2024;24(1):139. 10.1186/s12909-024-05114-w . Thoma B, Karwowska A, Samson L, Labine N, Waters H, Giuliani M, et al. Emerging concepts in the CanMEDS physician competency framework. Can Med Educ J. 2023;14(1):4–12. 10.36834/cmej.75591 . Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table41.docx SupplementaryFile1.pdf SupplementaryFile2.docx SupplementaryFile3.docx SupplementaryFile4.docx SupplementaryFile5.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor invited by journal 30 Oct, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 23 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7934532","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559896309,"identity":"c0596d6e-73a3-4206-9e41-1c996dc7a7a3","order_by":0,"name":"Catherine Fitzgerald","email":"","orcid":"","institution":"RCSI University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Fitzgerald","suffix":""},{"id":559896311,"identity":"050a9d8a-e5c6-4522-a93c-81517bc80ee4","order_by":1,"name":"Nicola Pagnucci","email":"","orcid":"","institution":"University of 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08:59:17","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":58387,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7934532/v1/92956b568bbb17600059cfcf.docx"},{"id":98780092,"identity":"667b9724-8e38-4b4b-8895-3fe8a7138140","added_by":"auto","created_at":"2025-12-22 12:31:02","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":57311,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7934532/v1/2953e5b892f6038cc0bcaa79.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying and assessing expertise gaps in healthcare education across Europe: a cross-sectional study ","fulltext":[{"header":"Background","content":"\u003cp\u003eThe healthcare sector across Europe faces persistent disparities in expertise, impacting on the quality of education and patient care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These inequities involve all healthcare disciplines. Projects like SAFE EUROPE have highlighted significant variations in healthcare education across Europe, particularly in smaller and low-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In line with the United Nations Sustainable Development Goal 4 \u0026lsquo;Ensuring inclusive and equitable quality education\u0026rsquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the SAFE EUROPE project highlighted insufficient access to specialised expertise and practical training in some countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This leads to disparities in the expertise of healthcare professionals, impacting on the quality and equity of patient care [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Expertise gaps have been reported for several healthcare professions receiving less comprehensive training, including radiotherapy educators who often lack peer support and struggle to stay abreast with technological advances [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; nursing programmes facing shortages of qualified faculty [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; nutritionists and dietitians encountering educational barriers that limit their contribution to sustainable food systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These issues are compounded by outdated curricula, insufficient funding, and limited access to equipment and clinical training. Small academic teams may lack the expertise to cover all areas, limiting student exposure. Brain drain further worsens this situation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSkill gaps increasingly include transversal and green competencies. Green skills involve values and knowledge that support sustainable healthcare, while transversal skills such as communication, teamwork, and critical thinking are essential for collaborative practice [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite growing awareness of these needs, studies that systematically map expertise gaps across knowledge, practice, research, and teaching in a multi-professional European context are lacking. Such mapping is crucial to inform targeted educational strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eDue to the absence of a single model that comprehensively addressed skill gaps in the domains of knowledge, practice, research, and teaching simultaneously, the survey development process began with brainstorming among our multidisciplinary research team members. To guide the brainstorming session, various existing frameworks such as Bloom\u0026rsquo;s Taxonomy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] (for knowledge and teaching), the Dreyfus Model of Skill Acquisition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] (for practice and teaching), Boyer\u0026rsquo;s Model of Scholarship [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (for research and teaching), and the Tuning Project [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] (for skills and knowledge gaps between academic offerings and workplace demands) were considered. Four key skill categories were also identified: hard, transversal, digital, and green, to be considered within the domains of knowledge, practice, research, and teaching. Based on this integrated approach and informed by current challenges and previous projects on expertise gaps [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we collaboratively developed and refined the survey questions (Supplementary File 1).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo identify expertise gaps in knowledge, practice, research, and teaching methods in healthcare professional education across Europe. A secondary objective was to develop and validate a survey that effectively measured expertise gaps in healthcare professionals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDesign\u003c/h3\u003e\n\u003cp\u003eThis study employed a cross-sectional design using an online survey methodology. Informed by a pragmatist research philosophy, the study focused on practical, context-sensitive insights into healthcare education across Europe.\u003c/p\u003e\n\u003ch3\u003eClinical trial number\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eEthics statement\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from the local Research Ethics Committee. All participants provided informed consent before accessing the survey. The survey was anonymous, with no identifiable data collected, and procedures complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eEligible participants were healthcare professionals and educators involved in teaching or clinical training in Europe, including physicians, nurses, physiotherapists, medical physicists, radiographers, and public health professionals. Open recruitment used a non-probabilistic sampling strategy. Survey links were disseminated via email and social media. Data were collected anonymously through Easy Feedback (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://easy-feedback.com/\u003c/span\u003e\u003cspan address=\"https://easy-feedback.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) from July to September 2024. There were no exclusion criteria to facilitate a broad multidisciplinary representation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cp\u003ePrimary outcome variables were self-reported expertise gaps in four academic domains: Knowledge, Practice, Research, and Teaching Methods. Within each domain, gaps were assessed across Hard, Transversal, Digital, and Green skills, using a 10-point Likert scale (10\u0026thinsp;=\u0026thinsp;greater gap). At the end of the questionnaire, there was an open-ended question where participants could briefly describe any additional expertise gaps, enabling to collect some descriptive qualitative data. (Supplementary File 1)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe validation process\u003c/h3\u003e\n\u003cp\u003eContent validity was assessed by eight experts using a 4-point Likert scale. Item-level (I-CVI) and scale-level (S-CVI) indices were calculated. Face validity was evaluated by ten professionals who reviewed the clarity and comprehensibility. The final survey had five sections: Demographics, Knowledge, Practice, Research, and Teaching Methods. Participants could also suggest additional gaps.\u003c/p\u003e\n\u003ch3\u003eReliability and construct validity\u003c/h3\u003e\n\u003cp\u003eReliability was assessed using Cronbach\u0026rsquo;s alpha (\u0026ge;\u0026thinsp;0.80 threshold). Confirmatory Factor Analysis (CFA) evaluated the instrument\u0026rsquo;s construct validity, confirming the alignment between theoretical constructs and survey items.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample size\u003c/h2\u003e \u003cp\u003eNo \u003cem\u003ea priori\u003c/em\u003e sample size was calculated. A total of 230 responses were deemed adequate for tool validation and CFA, following the 5\u0026ndash;10 participants-per-item guideline. Post hoc analysis showed good internal consistency and model fit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eCorrelation analysis examined links between demographics and perceived gaps. Pearson\u0026rsquo;s or Spearman's coefficients were used depending on variable type. Logistic regression explored predictors of perceived gaps. Qualitative responses were analysed to identify themes. All analyses were conducted using Jamovi (Version 2.6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 230 participants responded to the survey, of which 67% women (n\u0026thinsp;=\u0026thinsp;153) and 33% men (n\u0026thinsp;=\u0026thinsp;77). Due to the open distribution strategy via social media and professional networks, the total number of individuals reached could not be determined, making it impossible to calculate a response rate. The average age was 45.8 years (SD\u0026thinsp;=\u0026thinsp;12.5), the average length of work experience was 20.5 years (SD\u0026thinsp;=\u0026thinsp;11.9), while the average time in the current role was 12.4 years (SD\u0026thinsp;=\u0026thinsp;8.83). The main geographical areas included Eastern Europe, Northern Europe, Southern Europe, and Western Europe. Various professional categories were represented, including medical doctors (15.2%, n\u0026thinsp;=\u0026thinsp;35), nurses (13.9%, n\u0026thinsp;=\u0026thinsp;32), allied health professionals (57.0%, n\u0026thinsp;=\u0026thinsp;131), academics (7.0%, n\u0026thinsp;=\u0026thinsp;16) and \u0026lsquo;others\u0026rsquo; (7.0%, n\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e \u003cp\u003eA total of 53 (23%) participants replied to the open-ended question at the end of the questionnaire with a brief description of any additional gaps.\u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGeographical area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYears of work experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYears in current role\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003cp\u003e(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003cp\u003e(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003cp\u003e(8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e153\u003c/p\u003e \u003cp\u003e(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77\u003c/p\u003e \u003cp\u003e(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003cp\u003e(6.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003cp\u003e(4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003cp\u003e(3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003cp\u003e(2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003cp\u003e(4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003cp\u003e(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNorthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003cp\u003e(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003cp\u003e(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003cp\u003e(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e(12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003cp\u003e(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003cp\u003e(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003cp\u003e(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003cp\u003e(9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003cp\u003e(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003cp\u003e(9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003cp\u003e(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003cp\u003e(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003cp\u003e(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003cp\u003e(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003cp\u003e(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003cp\u003e(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003cp\u003e(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003cp\u003e(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003cp\u003e(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003cp\u003e(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003cp\u003e(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003cp\u003e(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003cp\u003e(7.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97\u003c/p\u003e \u003cp\u003e(42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e(19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003cp\u003e(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003cp\u003e(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003cp\u003e(9.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003cp\u003e(9.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003cp\u003e(9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003cp\u003e(7.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003cp\u003e(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003cp\u003e(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003cp\u003e(7.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e(26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e(11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003cp\u003e(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003cp\u003e(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.89\u003c/p\u003e \u003cp\u003e(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003cp\u003e(6.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003cp\u003e(6.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003cp\u003e(7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003cp\u003e(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003cp\u003e(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.22\u003c/p\u003e \u003cp\u003e(7.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003cp\u003e(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003cp\u003e(4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003cp\u003e(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003cp\u003e(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003cp\u003e(7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003cp\u003e(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003cp\u003e(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.41\u003c/p\u003e \u003cp\u003e(8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e(26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003cp\u003e(4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003cp\u003e(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003cp\u003e(9.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003cp\u003e(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003cp\u003e(5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003cp\u003e(9.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003cp\u003e(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003cp\u003e(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003cp\u003e(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003cp\u003e(6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003cp\u003e(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003cp\u003e(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003cp\u003e(9.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003cp\u003e(7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003cp\u003e(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003cp\u003e(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003cp\u003e(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003cp\u003e(6.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003cp\u003e(3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003cp\u003e(2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003cp\u003e(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003cp\u003e(5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003cp\u003e(3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003cp\u003e(6.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003cp\u003e(20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003cp\u003e(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003cp\u003e(2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMedical doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAllied health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAcademic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReliability and Construct Validity Analysis\u003c/h2\u003e \u003cp\u003eThe survey demonstrated strong internal consistency across all domains: Knowledge (α\u0026thinsp;=\u0026thinsp;0.877), Practice (α\u0026thinsp;=\u0026thinsp;0.920), Research (α\u0026thinsp;=\u0026thinsp;0.929), and Teaching (α\u0026thinsp;=\u0026thinsp;0.960).\u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability Analysis Internal Consistency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eScale Reliability Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCronbach's α\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCFA confirmed construct validity, whereby all factor loadings were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating robust psychometric properties.\u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eFactor Loadings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndicator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKNOWLEDGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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\u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEACHING METHODS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e[INSERT FIGURE 1 HERE]\u003c/p\u003e \u003cp\u003eTherefore, this tool was considered reliable and validated to measure expertise gaps of healthcare professionals and was used to assess these gaps across Europe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExpertise gaps of healthcare professionals across Europe\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eExpertise Gaps in Knowledge\u003c/h2\u003e \u003cp\u003eDigital (mean: 5.04) and green skills (mean: 6.16) constituted the most critical gaps across all regions. Additional gaps were noted in emerging medical fields (e.g., AI, radiobiology) and in transversal competencies, such as leadership and critical thinking. (Supplementary File 2)\u003c/p\u003e \u003cp\u003eThrough the open-ended question, participants highlighted gaps in specialised areas such as advanced medical imaging, radiobiology, prosthodontics, molecular pathology, and AI in radiology, indicating the need for greater expertise in emerging technologies. Gaps in transversal skills\u0026mdash;including leadership, communication, and critical thinking\u0026mdash;were also reported. In occupational therapy and nursing, knowledge gaps were reported in community-based care, geriatrics, paediatrics, and digital health literacy. Broader systemic challenges, including outdated curricula, limited post-graduate programmes, and misalignment between theory and clinical practice, further underscored the need for targeted, interdisciplinary, and technologically informed education across healthcare professions and regions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExpertise Gaps in Practice\u003c/h2\u003e \u003cp\u003ePractice gaps reflected those in knowledge, where green (mean: 5.96) and digital skills (mean: 5.03) again scored highest. Respondents reported insufficient clinical training, limited exposure to technology, and underdeveloped interprofessional collaboration. (Supplementary File 3)\u003c/p\u003e \u003cp\u003eThrough the open-ended question, participants highlighted difficulties translating theory into clinical practice, due to limited hands-on training, inadequate facilities, and scarce exposure to advanced techniques. There was also a need to integrate digital health, AI, and sustainability into care delivery processes. Challenges also included poor interprofessional collaboration, cultural competence, and gaps in leadership and decision-making. These findings underline the importance of clinical supervision, experiential learning, and updated training to bridge the theory\u0026ndash;practice divide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExpertise Gaps in Research\u003c/h2\u003e \u003cp\u003eMain gaps were reported in research methods, especially among respondents with postgraduate education. Issues included lack of training, limited access to resources, and competing clinical responsibilities. Green (mean: 6.27) and digital skills (mean: 5.59) scored highest. (Supplementary File 4)\u003c/p\u003e \u003cp\u003eParticipants reported widespread gaps in research skills, including methodology, critical appraisal, and statistics. Barriers included limited funding, mentorship, lab access, and complex procedures for ethics approval. Emerging fields such as AI, sustainability, and disaster preparedness were underrepresented, alongside gaps in public and mental health research. Limited postgraduate training and overreliance on qualitative approaches, especially in nursing, further reduced research capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExpertise Gaps in Teaching Methods\u003c/h2\u003e \u003cp\u003eTeaching methods showed gaps in green (mean: 6.05) and digital skills (mean: 5.33) (Supplementary File 5). Outdated pedagogies, insufficient use of digital tools, and inadequate training in innovative teaching approaches (e.g., simulation, flipped classrooms) were frequently mentioned.\u003c/p\u003e \u003cp\u003eParticipants reported gaps related to outdated teaching approaches, with limited use of innovative methods such as flipped classrooms, simulation, and digital tools. Many educators lacked formal pedagogical training and exposure to student-centred strategies. Key barriers included scarce resources, weak links between academic and clinical teaching, and underrepresentation of emerging topics like AI, digital health, and multicultural competencies. More interactive, experiential learning was recommended.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eOther expertise gaps\u003c/h2\u003e \u003cp\u003eAdditional challenges included a lack of recognition for certain professions (e.g., occupational therapists, and orthotics and prosthetics), inadequate CPD frameworks, and a disconnect between research and practice. Participants also identified structural issues in educational design and delivery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation, Comparative, and Regression Models\u003c/h2\u003e \u003cp\u003eNo significant correlations or group differences were found through regression, ANOVA, or correlation tests. This suggests widespread agreement across roles, regions, and demographic variables on the nature of the expertise gaps.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe aimed to identify expertise gaps in healthcare education across Europe and validate a tool for mapping these gaps. The findings confirmed widespread gaps across four domains\u0026mdash;knowledge, practice, research, and teaching methods\u0026mdash;particularly in digital and green skills. The new tool demonstrated strong reliability and validity in assessing these dimensions.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and implications for practice\u003c/h2\u003e \u003cp\u003eOverall, we found compelling evidence of persistent and systemic challenges in healthcare professional education across Europe. The main expertise gaps regarded digital (mean: 5.04) and green skills (mean: 6.16), reflecting the increasing integration of technology in clinical practice and the imperative for environmentally sustainable healthcare [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These gaps were observed not only in knowledge but also in practice, teaching, and research, underscoring the pervasive nature of these needs.\u003c/p\u003e \u003cp\u003eTransversal competencies\u0026mdash;such as leadership, communication, adaptability, and critical thinking\u0026mdash;were also underdeveloped (mean: 4.76), aligning with prior studies that emphasized the need for broader skill sets in modern healthcare [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The identified gaps reflect the difficulty of embedding these competencies within traditional curricula, especially in under-resourced institutions, where clinical and academic staff often lack pedagogical support or time to innovate [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifficulties translating knowledge into practice were very pronounced. Practice gaps were often linked to insufficient clinical exposure and hands-on training, which are essential for bridging theory-practice gap [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings reinforce the importance of experiential learning and simulation-based methods as pedagogical strategies for developing skills and confidence among students and early-career professionals.\u003c/p\u003e \u003cp\u003eRespondents from Eastern Europe reported more widespread gaps, especially at master's level education. While this finding aligns with prior research indicating disparities in education quality across European regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the small size of this subgroup and lack of statistical significance call for cautious interpretation.\u003c/p\u003e \u003cp\u003eThe limited integration of emerging technologies such as artificial intelligence (AI), digital health, and green innovations into curricula and practice highlighted a lag between healthcare innovation and education. Similar patterns have been observed in literature, where AI and sustainability are often neglected in healthcare training despite their growing importance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the lack of association between socio-demographic variables (i.e. age, role, experience) and perceived gaps suggested that these issues are pervasive and shared across professions and regions, further underscoring the systemic nature of our findings.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eComparison with previous studies\u003c/h2\u003e \u003cp\u003eOur findings are consistent with the SAFE EUROPE project, which identified substantial variations in healthcare education across Europe, especially in smaller or low-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with widespread presence educational inequalities, especially regarding green and digital skills.\u003c/p\u003e \u003cp\u003eOther studies have highlighted the shortage of faculty with advanced degrees in nursing and radiotherapy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our study extended those insights by showing that the problem affects also related fields such as occupational therapy, dietetics, and medical imaging. The alignment with the CanMEDS framework [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which emphasizes roles such as communicator, collaborator, and leader, also reinforces the call for curricula that include not only technical but also interpersonal and leadership skills.\u003c/p\u003e \u003cp\u003eFurthermore, earlier research has highlighted limitations in existing pedagogical strategies, such as overreliance on traditional lectures and few educators prepared in innovative teaching methods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These shortcomings were confirmed in our study, especially regarding limited use of simulation, flipped classrooms, and digital tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe sampling strategy relied on open online dissemination, resulting in a self-selected sample. This approach may have led to response, as those with more interest in the topic were more likely to participate. To address this, the survey was widely shared across diverse professional and geographic networks. Anonymity helped reduce social desirability bias, although self-reporting bias remained a limitation.\u003c/p\u003e \u003cp\u003eFurthermore, since the survey was distributed via professional networks and social media, the total number of individuals reached was unknown, making it impossible to calculate the response rate. The small sample size in some geographic subgroups limited the strength of comparative analyses across regions or professions.\u003c/p\u003e \u003cp\u003eAnother limitation was the use of self-reported data, which is inherently subjective and may not always reflect actual expertise levels. Despite these limitations, the study provides valuable insights across healthcare professions and European regions. These methodological decisions are consistent with a pragmatist approach to research, which values contextualised, actionable findings over methodological idealism [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eGeneralizability\u003c/h2\u003e \u003cp\u003eWhile our findings are not statistically generalisable to all European healthcare professionals, the broad representation of professions and regions supports their relevance and usefulness. The consistency of responses across demographic and geographic groups enhances the external validity of the results. This survey may also be applicable to other international settings facing similar challenges.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlighted the need to address gaps in knowledge, practice, research, and teaching methods within healthcare education. Digital and green skills emerged as key areas requiring curricular and pedagogical innovations. Although this was an initial step, this study yielded useful insights to inform educational planning and support more equitable, future-ready learning environments.\u003c/p\u003e \u003cp\u003eFurther research is recommended to gain a deeper understanding of the barriers and opportunities related to bridging expertise gaps. Additionally, longitudinal studies could explore whether interventions such as digital platforms or educator training programs can effectively reduce gaps over time. In addition, based on our results, educational institutions are encouraged to consider incorporating digital and green competencies more systematically, alongside pedagogical training for faculty and opportunities for simulation-based and interprofessional learning.\u003c/p\u003e \u003cp\u003eThe ECHOES platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.echoesplatform.eu\u003c/span\u003e\u003cspan address=\"http://www.echoesplatform.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) holds the potential to support these efforts by promoting access to specialised expertise and fostering collaborative, cross-regional educational opportunities. By encouraging dialogue and expertise sharing among healthcare educators and professionals, the platform could contribute to a more inclusive and responsive European educational landscape.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI \u0026ndash; Artificial Intelligence\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eANOVA - Analysis of Variance\u003c/p\u003e\n\u003cp\u003eCFA \u0026ndash; Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eCPD \u0026ndash; Continuing Professional Development\u003c/p\u003e\n\u003cp\u003eECHOES - ExChange Of ExpertiSe in healthcare professionals\u0026rsquo; education\u003c/p\u003e\n\u003cp\u003eEU \u0026ndash; European Union\u003c/p\u003e\n\u003cp\u003eI-CVI \u0026ndash; Item level Content Validity Index\u003c/p\u003e\n\u003cp\u003eS-CVI \u0026ndash; Scale level Content Validity Index\u003c/p\u003e\n\u003cp\u003eSD \u0026ndash; Standard Deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the European Union for co-funding this study through the Erasmus+ ECHOES Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCatherine Fitzgerald, Nicola Pagnucci, and Giuseppe Aleo contributed to the conception and design of the study, the methodology, acquisition of data, analysis and interpretation of data, validation, drafting the paper and final approval of the version to be published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNiamh Walsh contributed to the conception, and design of the study, the methodology, acquisition of data, and the drafting of the paper.\u003c/p\u003e\n\u003cp\u003eMarjorie Bonello, Petra Jones and Rachael Agius contributed to the conception and design of the study, the methodology, and the final review of the manuscript.\u003c/p\u003e\n\u003cp\u003eJos\u0026eacute; Guilherme Couto contributed to the conception and design of the study, project administration, funding acquisition, the methodology, supervision and final review of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was co-funded by the European Union through the Erasmus+ Programme, within the framework of the project \u0026ldquo;ExChange Of ExpertiSe in healthcare professionals\u0026rsquo; education\u0026rdquo; (ECHOES) [Project number: 2023-1-MT01-KA220-HED-000156744]. Disclaimer: Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or European Union Programmes Agency (EUPA). Neither the European Union nor the granting authority can be held responsible for them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article. However, any further information is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Research Ethics Committee of the Faculty of Health Sciences at the University of Malta (Reg. N. FHS-2024-00049). All participants provided informed consent before accessing the survey. The survey was anonymous, with no identifiable data collected, and procedures complied with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbimbola S, van de Kamp J, Lariat J, Rathod L, Klipstein-Grobusch K, van der Graaf R, et al. Unfair knowledge practices in global health: a realist synthesis. 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An evaluation of knowledge of circular economy among Therapeutic Radiographers/Radiation Therapists (TR/RTTs): Results of a European survey to inform curriculum design. Radiography. 2023;29(2):274\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.radi.2022.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.radi.2022.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagel DA, Penner JL, Halas G, Philip MT, Cooke CA. Exploring experiential learning within interprofessional practice education initiatives for pre-licensure healthcare students: a scoping review. 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Can Med Educ J. 2023;14(1):4\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.36834/cmej.75591\u003c/span\u003e\u003cspan address=\"10.36834/cmej.75591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Expertise gaps, healthcare education, instrument validation, cross-sectional study, factor analysis","lastPublishedDoi":"10.21203/rs.3.rs-7934532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7934532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDisparities in healthcare professional education across Europe generate expertise gaps, impacting negatively on patient care. Although previous studies have identified significant variations in the availability of educational resources, access to specialized training, and distribution of qualified educators, none have identified practical solutions to address these gaps. This study aimed at developing and validating a tool for identifying expertise gaps in healthcare education and assessing these gaps across Europe.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted between April-September 2024 involving healthcare professionals and academics. The survey, developed by a multidisciplinary team within an EU-funded project, explored expertise gaps in hard, transversal, digital, and green skills in the domains of: \u0026lsquo;knowledge\u0026rsquo;, \u0026lsquo;practice\u0026rsquo;, \u0026lsquo;research\u0026rsquo;, and \u0026lsquo;teaching methods\u0026rsquo;. Face validity, reliability and construct validity were assessed using Cronbach's alpha and confirmatory factor analysis (CFA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 230 responses were collected. Gaps in digital and green skills were identified across all domains. Research-related gaps were related to methodological knowledge and skills. Gaps in teaching methods included lack of innovative and student-centred approaches. The tool demonstrated strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026ge;\u0026thinsp;0.80), and Confirmatory Factor Analysis supported its construct validity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis validated tool can be used to map academic expertise gaps in healthcare education at local, national and international levels. Targeted interventions in digital and green competencies, innovative pedagogy, and research training are needed. The tool offered a practical starting point for identifying skill gaps, aligning with the United Nations Sustainable Development Goals and Europe\u0026rsquo;s commitment to high-quality, inclusive education.\u003c/p\u003e","manuscriptTitle":"Identifying and assessing expertise gaps in healthcare education across Europe: a cross-sectional study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:59:12","doi":"10.21203/rs.3.rs-7934532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"289311498764787847146023220630255053276","date":"2026-05-20T04:07:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189215615148622408208507146718902423336","date":"2026-05-19T02:42:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T13:55:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233641323200618185156624681491214695750","date":"2025-12-12T13:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T07:03:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T10:04:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T10:30:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T10:28:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-10-23T17:52:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"781b2f74-8231-44f3-8f3d-b2ac5daed021","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"289311498764787847146023220630255053276","date":"2026-05-20T04:07:55+00:00","index":96,"fulltext":""},{"type":"reviewerAgreed","content":"189215615148622408208507146718902423336","date":"2026-05-19T02:42:05+00:00","index":95,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T08:59:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:59:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7934532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7934532","identity":"rs-7934532","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}