QualiComp: Development and Initial Validation of a Competency Questionnaire for Quality Improvement in Health Services

QualiComp: Development and Initial Validation of a Competency Questionnaire for Quality Improvement in Health Services | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article QualiComp: Development and Initial Validation of a Competency Questionnaire for Quality Improvement in Health Services Eliane Pereira Silva, Selma Souza Bruno, Ana Carolina Patrício Albuquerque Sousa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9016562/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Successful quality improvement (QI) projects hinge on professionals' competencies to diagnose, implement, and sustain change. Objective To develop and provide initial validation evidence for QualiComp, a competency questionnaire for QI in health services. Methods We built a competency model combining widely used approaches in Brazil (diagnostic-oriented cycles) and international QI methods, explicitly integrating attitudes/beliefs, technical-analytical, and behavioral (soft) skills. Content validity was assessed by a two-round Delphi with experts, using item- and scale-level Content Validity Indexes (CVI). Cognitive interviews (n = 10) evaluated comprehensibility and response burden. The 31-item prototype was administered to a convenience sample of healthcare students and professionals (n = 155). Using polychoric correlations and WLSMV estimator with oblique rotation, we conducted exploratory factor analysis (EFA) to assess structural validity; factor retention combined fit indices, explained variance, and interpretability. Results Scale-level CVI was high across criteria (relevance 0.96; adequacy 0.97; usefulness 0.97; clarity 0.95). Cognitive interviews indicated good comprehensibility and ~ 7-minute completion time. EFA supported a parsimonious three-factor solution - beliefs/attitudes (4 items), technical-analytical competencies (19 items), and behavioural skills (7 items) - with adequate fit (e.g., RMSEA 0.077; CFI 0.954; TLI 0.943; SRMR 0.075) and improved structure after removing one misfitting item. Although four- and five-factor models yielded slightly better global fit, they produced conceptually weak/sparse factors and higher cross-loadings; therefore, we retained a parsimonious three-factor structure. Conclusion QualiComp shows promising evidence of content validity, acceptability, and structural validity, offering an integrated view of QI competencies. proving to be a promising and reliable instrument for assessing attitudes, technical, and It can support formative diagnosis, training design, and competency monitoring in QI cycles. Further studies should confirm the structure (CFA), test invariance, reliability, and responsiveness. Health Care Quality Quality Improvement Professional competence Introduction Quality Improvement (QI) in health services is essential to strengthen health systems and deliver safer, person-centered, and resilient care ( 1 – 3 ). In Brazil’s Unified Health System (SUS), a public, universal, and equity-oriented system, building institutional and professional capacity for continuous QI is strategic for the effectiveness of public policy and for the right to health. Preparing services and teams for successful improvement requires more than isolated projects; it depends on professionals’ competencies and on supportive organizational contexts. Aligned with recommendations from the National Academy of Science, Engineering and Medicine, health systems should train professionals capable of delivering person-centered care, using evidence-based practice, adopting a continuous improvement stance, and prioritizing equity and safety ( 4 , 5 ). In this equation, valuable resources ( 6 ) include domains such as communication, personal attributes, relationships, alongside the principles and practices. Recently, many studies have demonstrated that patient engagement impacts the healthcare results and is recognized as an improvement of quality of care ( 7 , 8 ) The Model for Understanding Success in Quality (MUSIQ) underscores the role of clinical microsystems and the interplay between motivation and competence in achieving results ( 9 ). Complementarily, the COM-B model (Capability, Opportunity, Motivation → Behavior) clarifies how capability and motivation shape the behaviors that sustain change ( 10 ). Prior work by our group has examined motivational determinants relevant to QI engagement ( 11 ). Several instruments have been proposed to assess QI-related knowledge, skills, and attitudes. The Quality Improvement Knowledge Application Tool (QIKAT) focuses on the ability to formulate aims, measures, and changes aligned with the Institute for Healthcare Improvement (IHI) Model for Improvement ( 12 ); the Mayo Evaluation of Reflection on Improvement Tool (MERIT) targets reflective domains ( 13 ); and the Beliefs, Attitudes, Skills, and Confidence in Quality Improvement Scale (BASiC-QI) offers a multidimensional self-assessment ( 14 ). However, most tools are anchored in North American paradigms and emphasize limited sets of competencies. In Brazil, QI education and practice have been strongly influenced by diagnostic-oriented cycles rooted in Juran’s quality improvement ( 15 – 19 ). This approach foregrounds structured problem diagnosis—process mapping, cause-and-effect analysis, Pareto analysis, and stratification—before setting aims and testing changes ( 16 , 20 ). By contrast, the IHI framework promotes a rapid approach starting from aims, measures, and changes, tested through Plan-Do-Study-Act (PDSA) cycles ( 20 ). Both approaches are widely used; in complex settings such as the SUS, teams frequently need both rigorous diagnostic capability and disciplined testing to achieve reliable change. International models of clinical governance and audit similarly emphasize diagnostic cycles as a core component of quality management ( 21 – 23 ). Despite this landscape, remains a gap for an instrument that (i) integrates competencies from both the diagnostic-oriented tradition and the rapid approach, and (ii) explicitly assess behavioral (non-technical) skills—such as leadership, communication, and teamwork now recognized as critical to sustaining change ( 11 , 24 – 26 ). Addressing this gap may support training, mentoring, and deployment of QI initiatives in resource-constrained, high-turnover environments that are typical of many SUS services. We therefore aimed to develop QualiComp, a competency questionnaire for QI in health services, and to gather initial evidence of content validity, acceptability, and structural validity in a student/professional sample. Materials and Methods Study design and setting We conducted a multi-step validation study to develop QualiComp and obtain initial evidence of its measurement properties: ( 1 ) instrument construction grounded in a competency framework; ( 2 ) expert content assessment using a two-round Delphi technique; ( 3 ) cognitive pre-testing; and ( 4 ) assessment of structural validity by exploratory factor analysis (EFA) alongside estimation of internal consistency. The study was carried out at the Federal University of Rio Grande do Norte (UFRN), Brazil. The recruitment process was conducted in two distinct phases. The first phase, involving the recruitment of experts for the Delphi technique, took place between August 18, 2023, and October 24, 2023. The second phase, comprising the administration of the questionnaire for assessment of structural validity, was carried out from June 26, 2024, to June 25, 2025. Step 1. Instrument construction Drawing on the Institute for Healthcare Improvement (IHI) Model for Improvement ( 20 , 27 , 28 ), the Spanish diagnostic-oriented improvement tradition ( 15 , 16 ), and evidence on behavioural (non-technical) skills in QI ( 24 , 25 ), we specified a competency model encompassing three domains: beliefs and attitudes toward QI; technical-analytical skills (eg. problem diagnosis and use of improvement tools), and behavioural skills (leadership, teamwork, communication). Item generation combined this competency model with the authors' teaching and implementation experience in the Brazilian Unified Health System (SUS), as well as the MUSIQ ( 9 ) and COM-B ( 10 ) frameworks, in order to explicitly integrate motivation and capability-related dimensions. The initial pool comprised 37 items across four sections: basic knowledge (4 items), attitudes (5 items), technical skills (21 items), behavioural skills (7 items). After content validation and cognitive testing (Steps 2 and 3), a 31-item version was retained for psychometric analyses. The English version of the instrument is available as Supplementary File 1. Step 2. Content validity (Delphi) Participants. We purposively recruited QI experts according to recommended criteria ( 29 ): formal training in QI and more than 5 years of experience in leading improvement projects and/or teaching QI. We invited 20 experts, anticipating non-response rates of 30–50% in the first round and 20–30% in the second round. Procedures. In Round 1, experts received: (i) the draft instrument (spreadsheet format), (ii) a summary of the underlying competency model, and (iii) detailed instructions for rating. Each item was rated on 5-point Likert scales for relevance, adequacy, usefulness, and clarity, and experts could provide qualitative comments and suggestions for rewording, deletion, or addition of items. In Round 2, we re-presented the revised items, highlighting changes (e.g. tracked changes or site-by-side versions) and requested experts to re-rate them using the same four criteria. Analysis. For each criterion, we calculated item-level and scale-level Content Validity Indexes (I-CVI, S-CVI) following Waltz, Strickland, and Lenz ( 30 ). A priori, items with I-CVI≤ 0.80 were candidates for revision or deletion, whereas an S-CVI ≥ 0.90 was considered evidence of adequate overall content validity. Qualitative comments were analysed thematically and adjudicated by the research team, prioritizing theoretical coherence with the competency model and clarity for the target audience. Step 3. Cognitive pre-testing We conducted cognitive interviews with 10 participants who resembled the target population (intentional sampling across students and health professionals). The objective was to assess comprehension of items and instructions, cognitive load, item order and flow, and potential redundancy. Interviews explored how respondents interpreted each item, how they chose response options, and any difficulties with terminology or format. Based on emerging issues, we planned iterative edits to wording, instructions, and item sequence until no new relevant problems were identified (problem saturation). Completion time for the full instrument was recorded to assess feasibility ( 31 ). Step 4. Structural validity Participants and data collection The 31-item QualiComp was administered to 155 respondents from three groups: medical students enrolled in a mandatory course on quality management in health services; students from the Master Degree in Quality Management in Health Services (Qualisaúde/UFRN); and staff from the university hospital. The questionnaire was administered primarily in digital format (email or application-based link) with paper forms available as backup. All items used a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). All participants provided informed consent prior to participation. Exploratory Factor Analysis We conducted an EFA using Mplus (version 7), under a structural equation modeling framework. Given the ordinal nature of the response scale, we used the robust weighted least squares estimator with mean and variance adjustment (WLSMV), which first estimates the polychoric correlation matrix and then minimizes residuals. We inspected the polychoric correlation matrix to identify potential issues of extreme collinearity (r > 0.90) and item redundancy. Items with excessive missingness or zero variance were flagged for further consideration. We used oblique Geomin rotation, allowing factors to be correlated, in line with the conceptual interdependence among beliefs/attitudes, technical-analytical, and behavioral domains. We tested 1–5 solutions. Factor retention decisions were based on a combination of: results of parallel analysis (polychoric correlations), global fit indices (Root Mean Square Error of Approximation - RMSEA, Comparative Fit Index - CFI, Tucker-Lewis Index - TLI, Standardized Root Mean Square Residual - SRMR), proportion of explained variance, and theoretical interpretability and parsimony of the factor structure. Factor loadings were evaluated for statistical significance at the 5% level (p < 0.05). For interpretation, each item was assigned to the factor on which it exhibited the highest statistically significant loading. Cross-loadings and items with low loadings were examined in terms of both statistical performance and conceptual relevance to decide on retention or exclusion. Ethical considerations The study was conducted in compliance with the ethical standards of the institutional and national research committees, specifically following Resolutions 466/12 and 510/16 of the Brazilian National Health Council (CNS), which regulate research involving human participants in Brazil. Furthermore, the protocol adhered to the principles of the Declaration of Helsinki (1964) and its subsequent amendments. This study was approved by the Research Ethics Committee of the Federal University of Rio Grande do Norte, Brazil (approval No. 6.099.338), approved on 3 July 2023. Informed consent was obtained from all participants. The study only proceeded after participants had read and signed the consent form, which was provided in either electronic or paper format. As the study did not include minors. Participation was voluntary and had no consequences for academic assessment or employment. Results Content Validity Fourteen experts participated in Round 1 and 12 in Round 2 of the Delphi process. In Round 1, most items met the a priori threshold for relevance (I-CVI ≥ 0.80); three items were slightly below this cut-off (Items 8, 9, and 26: I-CVI = 0.79 each). Based on qualitative feedback, we consolidated Items 13 and 14 due to overlap and refined the wording of several items, including reverse-worded items. In Round 2, agreement was high across all four criteria. Only Item 9 remained below the relevance threshold (I-CVI = 0.75). Given the overall convergence of ratings, stability of scores between rounds, and the conceptual relevance of Item 9, we decided to close the Delphi after Round 2 and retained Item 9 (with revised wording) for subsequent psychometric testing. Scale-level CVI values were high: 0.96 for relevance, 0.97 for adequacy 0.97, 0.97 for usefulness, and 0.96 for clarity, indicating strong overall content validity. Table 1 presents the characteristics of the Delphi experts, and Table 2 shows item-level CVI values by criterion and round. Reverse-worded items (Item 8 and 9) were recoded prior to the subsequent analyses. Table 1 Characterization of Delphi experts (n = 14). Interviewee profile n % Sex Female 7 50.0 Male 7 50.0 Undergraduate background Medicine 6 42.7 Nursing 2 14.3 Physiotherapy 2 14.3 Computer Science 1 7.1 Electrical Engineering 1 7.1 Pharmacy 1 7.1 Dentistry 1 7.1 Highest degree Specialist 1 7.1 Master's 3 21.4 Doctorate 6 42.9 Postdoctoral 4 28.6 Total 14 100.00 Table 2 Content Validity Indexes (CVI) by item and criteria in rounds 1 and 2. Item Relevance Adequacy Usefulness Clarity CVI1 CVI2 CVI1 CVI2 CVI1 CVI2 CVI1 CVI2 1. Strategies for evaluating healthcare in my health service 0.93 0.92 0.79 0.92 0.71 0.92 0.71 0.92 2. The role of healthcare professionals as part of a system that affects patient and family outcomes 0.93 0.92 0.86 0.92 0.93 1.00 0.79 1.00 3. The importance of variation and evaluation of the quality of healthcare 0.93 0.92 0.86 1.00 0.86 1.00 0.64 1.00 4. Approaches to change healthcare processes for the better 1.00 0.92 1.00 1.00 0.93 1.00 0.93 1.00 5. Continuous quality improvement is an essential part of the daily work of all healthcare professionals 1.00 0.92 0.93 1.00 0.93 1.00 1.00 1.00 6. I and others can contribute to improving the results of care in my health service 1.00 0.92 1.00 1.00 0.79 0.92 0.93 0.92 7. Changing healthcare for the better can lead to greater job satisfaction 1.00 1.00 1.00 1.00 0.93 1.00 1.00 1.00 8. Quality improvement projects increase the burden on healthcare professionals 0.79 0.83 1.00 1.00 1.00 1.00 0.86 1.00 9. In my service we always do the right things in health care; the problems are just structural 0.79 0.75 1.00 0.92 1.00 0.2 0.86 0.83 10. Identify opportunities for improvement in healthcare 1.00 1.00 1.00 1.00 0.93 1.00 1.00 1.00 11. Prioritize the most important opportunities for improvement in my local context 1.00 1.00 1.00 1.00 0.86 1.00 1.00 1.00 12. Use a consensus method to prioritize an improvement opportunity with my work team 0.93 1.00 0.93 1.00 0.93 1.00 0.93 1.00 13. Analyze the causes of problems on quality of care with the aim of improving 1.00 1.00 1.00 1.00 0.93 1.00 0.93 1.00 14. Use tools for analyzing causes (e.g. cause-effect diagram, why technique, etc.) 1.00 - 1.00 - 1.00 - 1.00 - 15. Map a health care process in order to analyze ways of optimizing it 1.00 1.00 1,00 1.00 1.00 1.00 1.00 1.00 16. Develop a criterion or indicator to measure the quality of health care 1.00 1.00 1.00 1.00 0.93 1.00 1.00 1.00 17. Collecting data on a criterion or indicator 1.00 0.92 1.00 0.92 0.93 0.92 0.79 0.83 18. Critically analyze the validity of criteria or indicators designed to measure the quality of health care 1.00 0.92 1.00 0.92 0.93 0.92 0.93 0.92 19. Set clear targets for improving health care (e.g. SMART, etc.) 1.00 1.00 1.00 1.00 0.93 1.00 0.93 1.00 20. Create a multi-faceted improvement intervention (e.g. steering diagram, affinity diagram, etc.) 1.00 0.92 1.00 0.92 0.93 0.92 0.86 0.83 21. Testing changes on a small scale before promoting their implementation (e.g. PDSA) 0.93 0.92 0.93 0.92 0.86 0.92 0.86 0.92 22. Use tools to monitor the implementation of improvement actions (e.g. Gantt chart, 5W2H, etc.) 1.00 0.92 1.00 0.92 1.00 0.92 0.93 0.83 23. Disseminate the improvements achieved in one service to a group of similar health services 1.00 0.92 1.00 0.92 0.93 0.92 0.93 0.92 24. Use a quality measure to estimate health service performance 1.00 1.00 0.93 1.00 0.93 1.00 0.86 1.00 25. Apply tools to analyze which criteria are priorities for improving quality (e.g. Pareto diagram) 0.93 1.00 0.93 1.00 0.86 1.00 0.93 0.92 26. Analyze whether there is an association between two variables to identify causes of poor quality (e.g. scatter diagram, etc.) 0.79 1.00 0.86 1.00 0.79 1.00 0.79 0.83 27. Stratify the results of an indicator to identify causes of poor quality 1.00 0.92 1.00 0.92 1.00 0.92 0.93 0.83 28. Construct a baseline in trend charts or control charts 1.00 1.00 1.00 1.00 1.00 1.00 0.93 1.00 29. Identify significant improvement by looking at trend graphs or control charts 1.00 1.00 1.00 1.00 1.00 1.00 0.86 0.92 30. Differentiating whether process data is stable or unstable 1.00 1.00 1.00 1.00 1.00 1.00 0.86 1.00 31. Selecting a good profile of participants for an improvement team 0.93 1.00 0.93 1.00 0.86 1.00 0.71 1.00 32. Defining responsibilities within an improvement team 1.00 1.00 1.00 0.92 0.93 0.92 1.00 1.00 33. Monitoring teamwork during a complete improvement project 0.93 1.00 1.00 1.00 0.93 1.00 1.0 1.00 34. Managing interpersonal conflicts 0.86 1.00 1.00 0.92 0.93 1.00 0.93 1.00 35. Activate the intrinsic motivation of team members 1.00 0.92 1.00 1.00 0.93 0.92 0.93 0.92 36. Encourage team members to speak up, question and propose ideas for quality improvement 0.93 1.00 1.00 1.00 0.93 1.00 0.93 1.00 37. Communicate openly with team members to solve problems when an improvement project runs into difficulties 1.00 1.00 1.00 1.00 0.93 1.00 1.00 1.00 Overall assessment 0.96 0.96 0.97 0.97 0.92 0.97 0.90 0.95 Notes: Item 14 was merged into item 13 after round 1; therefore, it was not re-rated in round 2. Table 1 - Characterization of Delphi experts (n = 14). Table 2 - Content Validity Indexes (CVI) by item and criteria in rounds 1 and 2 Following content approval, the research team reviewed all qualitative suggestions and consolidated overlapping content. Sections 1 (knowledge about quality improvement) and 2 (attitudes toward quality improvement) were merged, retaining items focused on beliefs and attitudes toward QI. Items 1 and 4, which addressed technical nuances already captured by items in the technical-skills domain, were removed. Items 5 and 6 showed similar content; we retained Item 5 because of clearer wording. Item 9, although below the relevance threshold in Round 2, was maintained due to its conceptual importance and earmarked for empirical testing. The resulting version taken forward to cognitive pre-test comprised 31 items organized into three sections: ( 1 ) beliefs/attitudes about QI, ( 2 ) technical-analytical skills, and ( 3 ) behavioural (soft) skills. Cognitive pre-testing Ten participants completed cognitive interviews. Mean completion time for the full instrument was seven minutes (range: 4–16 minutes), indicating good feasibility for educational and service contexts. Participants did not report comprehension difficulties or problems related to item order or response options. Only minor editorial standardization (e.g. wording consistency and layout) was applied. No content-related changes to items were required as a result of this step. Structural Validity (EFA) Model fit and factor retention The 31-item QualiComp was administered to 155 respondents from the three predefined groups. We estimated a series of one- to five-factor EFA solutions using polychoric correlations, the WLSMV estimator, and oblique Geomin rotation. Parallel analysis based on polychoric correlations supported a three-factor solution, which was also consistent with the underlying competency model. Table 3 summarises the global fit indices for models with one to five factors, considering both the full set of items and the solution after removing Item 5. Table 3 Model fit indices for 1–5-factor EFA solutions (all items vs. minus Item 5). Factors χ²(df) RMSEA [90%] CFI TLI SRMR Prob. RMSEA ≤ 0.05 A. All items 1 1527.293(434) 0.127 [0.121; 0.134] 0.845 0.834 0.143 0.000 2 952.204(404) 0.094 [0.086; 0.101] 0.922 0.910 0.105 0.000 3 686.895(375) 0.073 [0.065; 0.082] 0.956 0.945 0.075 0.000 4 534.427(347) 0.059 [0.049; 0.069] 0.973 0.946 0.058 0.069 5 438.212(320) 0.049 [0.037; 0.060] 0.983 0.976 0.053 0.559 B. Item 5 deleted 1 1519.30(405) 0.133 [0.126; 0,140] 0.841 0.829 0.146 0.000 2 935.12(376) 0.098 [0.090; 0.106] 0.920 0.908 0.106 0.000 3 667.77(348) 0.077 [0.068; 0.086] 0.954 0.943 0.075 0.000 4 519.29(321) 0.063 [0.053; 0.073] 0.972 0.962 0.058 0.018 5 420.13(295) 0.052 [0.040; 0.064] 0.982 0.974 0.052 0.361 Note. Estimation = WLSMV; rotation = geomin; data = polychoric correlations; response scale = Likert ( 1 – 5 ); n = 155; RMSEA 90% confidence interval in brackets; pclose reported as “Prob. RMSEA < 0.05”. Bold values indicate the selected factor solution. For the 31-item three-factor model, global fit indices were acceptable (RMSEA = 0.073, CFI = 0.956, TLI = 0.945, SRMR = 0.075). After removing Item 5, the three-factor solution showed a very similar fit (RMSEA = 0.077, CFI = 0.954; TLI = 0.943; SRMR = 0.075. Four- and five-factor solutions yielded marginal improvements in global fit (e.g., for the five-factor model without Item 5: RMSEA = 0.052, CFI = 0.982), but at the cost of conceptually weak or sparse factors, increased cross-loadings, and imbalance in the distribution of items across factors. On grounds of parsimony, theoretical coherence with the competency model, and interpretability, we retained the three-factor solution with Item 5 removed as the final structural model. Table 3 - Model fit indices for 1–5-factor EFA solutions (all items vs. minus Item 5). Item performance and factor loadings Table 4 presents the factor loadings for the initial three-factor solution including all items. In this model, Item 5 exhibited a negative primary loading (–0.22 on Factor 3) and instability across factors, suggesting poor conceptual alignment with the emergent structure. Unlike most items, which targeted competencies or attitudes, Item 5 emphasised a perceived adverse effect of QI (increased workload), which likely contributed to its inconsistent performance. Item 6 displayed notable cross-loadings on Factors 1 and 2. Table 4 Standardized factor loadings for the three-factor solution (all items). Item Factor 1 Factor 2 Factor 3 Main factor 1 0.786 0.004 0.002 Factor 1 2 0.759 0.144 -0.008 Factor 1 3 0.662 -0.011 0.135 Factor 1 4 0.469 0.204 -0.012 Factor 1 5 -0.056 0.029 -0.220 Factor 3 6 0.376 0.551 0.262 Factor 2 7 0.294 0.813 0.061 Factor 2 8 0.223 0.870 -0.065 Factor 2 9 0.118 0.813 -0.045 Factor 2 10 0.028 0.953 -0.099 Factor 2 11 -0.045 0.865 -0.074 Factor 2 12 -0.019 0.876 -0.023 Factor 2 13 0.162 0.757 0.099 Factor 2 14 -0.057 0.692 0.070 Factor 2 15 -0.001 0.644 0.237 Factor 2 16 -0.002 0.689 0.295 Factor 2 17 0.059 0.522 0.399 Factor 2 18 -0.186 0.622 0.218 Factor 2 19 -0.226 0.716 0.194 Factor 2 20 -0.386 0.737 -0.060 Factor 2 21 -0.474 0.748 0.005 Factor 2 22 -0.599 0.671 0.023 Factor 2 23 -0.583 0.735 -0.023 Factor 2 24 -0.318 0.726 0.060 Factor 2 25 0.008 0.098 0.768 Factor 3 26 -0.032 0.159 0.759 Factor 3 27 0.030 0.237 0.665 Factor 3 28 0.106 0.097 0.687 Factor 3 29 -0.093 -0.053 0.737 Factor 3 30 -0.034 -0.016 0.824 Factor 3 31 -0.079 0.006 0.714 Factor 3 After removing Item 5, the three-factor solution (Table 5 ) exhibited a cleaner and more interpretable pattern of loadings. Cross-loadings were reduced, and Item 6 loaded primarily on Factor 2, aligning with its intended classification as a technical-analytical skill. Table 5 Standardised factor loadings for the three-factor solution (minus item 5). Item Factor 1 Factor 2 Factor 3 Main factor 1 0.788 0.002 0.202 Factor 1 2 0.759 0.142 -0.006 Factor 1 3 0.661 -0.008 0.127 Factor 1 4 0.469 0.205 -0.015 Factor 1 6 0.374 0.556 0.251 Factor 2 7 0.293 0.693 0.056 Factor 2 8 0.220 0.874 -0.075 Factor 2 9 0.115 0.814 -0.049 Factor 2 10 0.024 0.958 -0.108 Factor 2 11 -0.048 0.867 -0.079 Factor 2 12 -0.022 0.878 -0.028 Factor 2 13 0.160 0.761 0.092 Factor 2 14 -0.059 0.697 0.060 Factor 2 15 -0.001 0.650 0.226 Factor 2 16 -0.002 0.695 0.284 Factor 2 17 0.058 0.528 0.388 Factor 2 18 -0.187* 0.624 0.214 Factor 2 19 -0.228 0.718 0.190 Factor 2 20 -0.388 0.736 -0.059 Factor 2 21 -0.476 0.750 0.000 Factor 2 22 -0.602 0.668 0.027 Factor 2 23 -0.586 0.732 -0.020 Factor 2 24 -0.320 0.727 0.057 Factor 2 25 0.010 0.101 0.763 Factor 3 26 -0.030 0.161 0.758 Factor 3 27 0.034 0.238 0.663 Factor 3 28 0.110 0.098 0.685 Factor 3 29 -0.087 -0.055 0.742 Factor 3 30 -0.029 -0.016 0.825 Factor 3 31 -0.073 0.003 0.717 Factor 3 The final configuration was as follows: Factor 1 – Beliefs and Attitudes toward Quality Improvement Comprised Items 1–4, all with loadings above 0.66, capturing respondents’ views on the importance and role of QI in routine professional practice. Factor 2 – Technical-Analytical Competencies Comprised Items 6–24, with most loadings near or above 0.70. This factor encompassed skills related to identifying opportunities for improvement, using diagnostic tools (e.g. cause-and-effect diagrams, Pareto analysis, stratification), constructing and interpreting indicators, designing and monitoring interventions, and using time-series or control charts. Factor 3 – Behavioral (Soft) Skills for Improvement Comprised Items 25–31, with strong and predominantly exclusive loadings (e.g., Item 30: 0.83; Item 31: 0.72). This factor reflected competencies in team selection and coordination, conflict management, activation of intrinsic motivation, psychological safety (speaking up and questioning), and open communication during QI projects. Table 4 - Standardized factor loadings for the three-factor solution (all items). Table 5 - Standardised factor loadings for the three-factor solution (minus item 5). Overall, the loading pattern was statistically robust and conceptually coherent with the a priori competency model. The statistical consistency of the structure was corroborated by eigenvalues and explained variance: the first three factors accounted for 42.4%, 11.6%, and 8.5% of the total variance, respectively, for cumulative variance explained of 62.5%. Additional factors contributed little and incremental variance and increased interpretive dispersion, reinforcing the choice of a three-factor solution. Removing Item 5 improved the overall structure by eliminating a source of conceptual noise, and reducing cross-loadings, thereby sharpening factor boundaries and yielding a more parsimonious and theoretically aligned measurement model for QualiComp. Discussion Principal’s findings We developed QualiComp, a competency questionnaire for QI in health services, and obtained initial evidence of its measurement properties. Content validity indices showed high agreement across criteria, cognitive testing indicated good acceptability (mean completion time ~ 7 minutes, no comprehension issues), and exploratory factor analysis supported a parsimonious three-factor structure aligned with the conceptual model: Beliefs/Attitudes (F1), Technical-Analytical Competencies (F2), and Behavioral/Soft Skills (F3). Although four- and five-factor solutions marginally improved global fit indices, they introduced conceptually weak or sparse factors and increased cross-loadings. We therefore retained the three-factor solution on grounds of parsimony and interpretability. The proportion of explained variance (62.5%) and the cleaner factor structure after removing one misfitting item further support the clarity and coherence of the configuration. Interpretation and theoretical alignment In complex health systems, both the multidimensional nature of quality and the choice of appropriate measurement tools remain central concerns for delivering high-quality services. Instruments that jointly assess technical capability and behavioral (non-technical) skills, such as leadership, communication, teamwork, and stress management, tend to relate more closely related to performance in academic and service settings. Evidence from systematic reviews of non-technical skills in complex clinical environments supports improvements in team behaviours, leadership, and communication, with tangible effects such as reduced response times in ICU cardiac arrest scenarios ( 32 ). QualiComp was designed to integrate motivational, technical, and behavioral components and, in this study, showed initial structural validity consistent with that intent. This integration is particularly relevant in settings with high staff turnover or frequent role rotation of roles, where competencies must be trained and reassessed at short intervals to approximate quality outcomes in care. In both real and simulated training environments, robust tools are needed to detect gaps and guide target adjustments - whether technical or behavioral - so that team performance can be improved in a systematic and iterative manner. Our final factor solution coheres with established theory. Factor 1 (Beliefs/Attitudes) captures willingness to improve, recognition of variation, and normalisation of improvement as part of everyday work - elements that map closely to the motivation component in COM-B ( 10 ). Factor 2 (Technical-Analytical Competencies) consolidates the operational core of QI (prioritising opportunities, cause analysis, process mapping, indicators, control charts, impact evaluation), aligning with the capability component in COM-B and with performance determinants emphasised by MUSIQ at the microsystem level ( 9 ). Factor 3 (Behavioral/Soft skills) brings together collaborative leadership, communication, teamwork, and conflict management—competencies repeatedly linked to reliability and safety in complex environments. Although models with four or five factors slightly improved global fit, they fragmented constructs and increased cross-loadings. From a measurement perspective, a model that sacrifices interpretability and practical usability for marginal gains in fit may be less useful for educational and managerial purposes. Retaining a three-factor configuration preserves conceptual coherence and supports the formative use of QualiComp in training and service settings. Motivational beliefs as a measurable target A distinctive contribution of this study is to explicitly positioning of beliefs and attitudes within a motivational domain, analysed alongside technical and behavioral competencies in the same measurement model. Beliefs frequently observed in practice - such as perceptions that QI increases workload or merely adds “more checklists” - surfaced in our data and loaded strongly on the motivational domain (loadings > 0.66 across four items). Prior studies hint at the same mechanism: with workload complaints undermining engagement in QI ( 33 ), and tensions between clinical time, administrative burden, and improvement activities acting as barriers to implementation ( 34 ). Together, these findings support treating motivational beliefs as a measurable and actionable target in QI capacity-building strategies, rather than as an unobserved or implicit background factor. The content validity process was also important for legitimacy and applicability. Experts from multiple Brazilian regions and Spanish-speaking contexts (Spain, Mexico) participated, in line with Delphi guidance that recommends diversity of origin and perspective to enhance judgement quality ( 35 ). This diversity likely contributed to item clarity and relevance across different service realities and organizational cultures. Parsimony and Formative Utility Although four- and five factor solutions yielded marginal gains in global fit indices, these came at the expense of conceptual coherence and parsimony. We therefore favoured the three-factor configuration to maximise interpretability and practical usability as a formative and managerial tool, consistent with the emphasis on actionable capability and motivation in COM-B and microsystem-focused implementation in MUSIQ ( 10 , 9 ). In QI programmes, simpler and clearly interpretable models reduce burden on teams and facilitate incorporation into routine workflows. This, in turn, supports cycles of training, reassessments, and feedback, thereby enhancing the formative utility of measurement ( 36 , 37 ). In this sense, the primary value of QualiComp is not the distal total score per se, but its capacity to guide continuous improvement through dimension-specific diagnosis and mentoring ( 36 , 37 ). Results can be used to identify which domain—motivational, technical-analytical, or behavioural—requires more investment, and to tailor curricula and supervision accordingly. Comparison with existing instruments There is a growing emphasis on non-technical (soft) skills across educational and organizational contexts, accompanied by dedicated assessment tools and systematic reviews ( 32 , 38 , 39 ). Evidence also links combinations of technical knowledge with skills in communication, management, emotional intelligence, and confidentiality to better organisational performance in healthcare ( 40 , 41 ). In line with this trend, previous studies have proposed integrating technical and behavioral competencies and have shown their influence on performance across clinical and surgical settings ( 42 , 43 ). From a training perspective, it is therefore reasonable that the quality of instruction - and ultimately service quality - be monitored with instruments that are sensitive to multiple competency axes rather than single-domain measures. Within this landscape, QualiComp advances beyond instruments that traditionally privilege one axis at a time ( 44 – 47 ) by integrating three domains in a single measure: attitudes and beliefs, technical-analytical competencies, and behavioural (soft) skills. Compared with tools that focus on narrower targets - such as reflective domains or application of aims/measures/changes - BASiC-QI ( 14 ) is among the most innovative we identified for encompassing more than one axis; still, it addresses two domains, whereas QualiComp captures three. This broader scope supports richer formative diagnoses to design training and mentorship pathways and enhances applicability in multiprofessional teams, while maintaining a brief administration time suitable for repeated use in educational and service contexts. Practical implications We envisage three immediate uses of QualiComp: Formative diagnosis by dimension, to plan curricula and on-the-job training based on the specific profile of beliefs, technical-analytical skills, and behavioural competencies in a given cohort or service. Mentoring allocation and goal setting by domain, for example emphasising behavioural skills in services with high staff turnover or psychological safety issues or strengthening analytical capability where complex process redesign is required). Longitudinal monitoring of competencies across QI cycles, enabling programmes to test responsiveness to training, track progress over time, and tailor supports where gaps persist. In addition to valid instruments for evaluating QI competencies, it is essential to train professionals and managers to use such tools as indicators for designing and adjusting training strategies. The results of QualiComp are most useful when interpreted by teams who can translate dimension-specific findings into concrete educational and organizational interventions. The statements and themes included in the instrument allow its application in undergraduate courses, specialist training programs, and collaborative improvement projects, as it covers knowledge and skills addressed in the methodologies widely used in Brazil, including diagnostic-oriented cycles and rapid-cycle improvement. Strengths, limitations and future research Strengths of this study include explicit theoretical grounding (COM-B; MUSIQ), a transparent content-validation process with experts from multiple regions and countries, appropriate methods for ordinal data (polychorics correlations, WLSMV estimator, oblique rotation), and evidence of operational feasibility (short completion time and good acceptability). The integrated scope across beliefs/attitudes, technical-analytical and behavioural domains is a practical advance for multiprofessional teams working in QI. Several limitations should be acknowledged. First, findings derive from a single-context convenience sample and rely on self-report Likert responses, which may limit generalizability and introduce social desirability or common-method variance. Second, cross-sectional design did not allow assessment of temporal stability (test-retest) or responsiveness to training and mentoring interventions. Third, we report initial structural validity based on EFA; confirmatory factor analysis (CFA) in an independent sample is still required. Fourth, we did not examine measurement invariance across subgroups (e.g. profession, sex/gender, experience) or convergent validity with external instruments or objective performance indicators. Finally, data were collected in Portuguese in a Brazilian academic-service setting; generalization to other languages, regions, and service configurations should be made cautiously until cross-cultural adaptation and validation studies are conducted. Future research should: (i) conduct CFA in independent, multicentre samples (e.g. undergraduate health courses and frontline services), with detailed fit indices and model refinement; (ii) test measurement invariance (configural, metric, scalar) across profession, sex/gender, and experience strata; (iii) extend evidence on reliability, including ω and α with confidence intervals and test–retest reliability over 2–4 weeks; (iv) examine responsiveness to training and mentoring programmes using pre-post and repeated measures designs; (v) investigate convergent and criterion-related validity through associations with external measures (e.g. BASiC-QI) and, when feasible, objective process or performance indicators; (vi) assess ceiling and floor effects and potential differential item functioning (DIF); (vii) explore item response theory (IRT) models to refine item hierarchy and information; and (viii) develop interpretation norms or cut-points by context (teaching vs. service, primary vs. hospital care) to support formative decision-making. Conclusion QualiComp provides an integrated, practical perspective on QI competencies, capturing Beliefs/Attitudes, Technical-Analytical skills, and Behavioral/Soft skills in a single, brief instrument. This study offers initial evidence of content validity, acceptability, and structural validity, supporting its use as a formative tool to diagnose needs, guide training and mentorship, and monitor competency development across QI cycles. Further confirmatory studies and longitudinal studies are needed to consolidate its measurement properties, including reliability, invariance, and responsiveness. In parallel, careful implementation and interpretation in educational and service settings may help QualiComp contribute to strengthening QI capacity and, ultimately, to improving the quality and safety of care. Declarations Acknowledgments We are grateful to The Federal University of Rio Grande do Norte for supporting the graduate program in improving the quality of health services. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The study was supported by the authors' host institutions as part of their standard research activities. Ethics approval and consent to participate. All methods were carried out in accordance with relevant guidelines and regulations. The study was conducted in compliance with the ethical standards of the institutional and national research committees, specifically following Resolutions 466/12 and 510/16 of the Brazilian National Health Council (CNS), which regulate research involving human participants in Brazil. Furthermore, the protocol adhered to the principles of the Declaration of Helsinki (1964) and its subsequent amendments. Ethical approval was formally granted by the Research Ethics Committee of the Onofre Lopes University Hospital at the Federal University of Rio Grande do Norte (HUOL-UFRN), under protocol number 6.099.338. Participation was strictly voluntary, and all individuals were informed of their right to withdraw from the study at any stage without penalty. Informed consent was obtained from all participants prior to data collection, utilizing either electronic or paper-based formats as appropriate. All collected data were handled with strict confidentiality and anonymity to protect the privacy of the participants. Consent for publication Not applicable. Author Contribution E.P.S, S.S.B,A.L.B.L, Z.A.S.G These authors contributed in the conception, supervision of the study, collection and analysis of data, statistical analysis, critical review and writing of the manuscript.A.C.P.A.S, P.J.M, M.R.F These authors contributed to the collection of data, interpretation, critical review and writing of the manuscript.All authors approved the final version of the manuscript. Data Availability The anonymized dataset supporting the findings of this study has been deposited in the Figshare repository. 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BMC Health Serv Res. 2021;21(1):588. Pereira RD, Alvim NA. Técnica Delphi no diálogo com enfermeiros sobre a acupuntura como proposta de intervenção de enfermagem. Escola Anna Nery. 2015;19(1):174–80. Stetler CB, Legro MW, Wallace CM, Bowman C, Guihan M, Hagedorn H, Kimmel B, Sharp ND, Smith JL. The role of formative evaluation in implementation research and the QUERI experience. J Gen Intern Med. 2006;21(Suppl 2):S1–8. Meyer GS, Nelson EC, Pryor DB, James B, Swensen SJ, Kaplan GS, Weissberg JI, Bisognano M, Yates GR, Hunt GC. More quality measures versus measuring what matters: a call for balance and parsimony. BMJ Qual Saf. 2012;21(11):964–8. McMullan RD, Urwin R, Sunderland N, Westbrook J. Observational tools that quantify nontechnical skills in the operating room: a systematic review. J Surg Res. 2020;247:306–22. Higham H, Greig PR, Rutherford J, Vincent L, Young D, Vincent C. 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In Brazil\u0026rsquo;s Unified Health System (SUS), a public, universal, and equity-oriented system, building institutional and professional capacity for continuous QI is strategic for the effectiveness of public policy and for the right to health.\u003c/p\u003e \u003cp\u003ePreparing services and teams for successful improvement requires more than isolated projects; it depends on professionals\u0026rsquo; competencies and on supportive organizational contexts. Aligned with recommendations from the National Academy of Science, Engineering and Medicine, health systems should train professionals capable of delivering person-centered care, using evidence-based practice, adopting a continuous improvement stance, and prioritizing equity and safety (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In this equation, valuable resources (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) include domains such as communication, personal attributes, relationships, alongside the principles and practices. Recently, many studies have demonstrated that patient engagement impacts the healthcare results and is recognized as an improvement of quality of care (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe Model for Understanding Success in Quality (MUSIQ) underscores the role of clinical microsystems and the interplay between motivation and competence in achieving results (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Complementarily, the COM-B model (Capability, Opportunity, Motivation \u0026rarr; Behavior) clarifies how capability and motivation shape the behaviors that sustain change (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Prior work by our group has examined motivational determinants relevant to QI engagement (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral instruments have been proposed to assess QI-related knowledge, skills, and attitudes. The Quality Improvement Knowledge Application Tool (QIKAT) focuses on the ability to formulate aims, measures, and changes aligned with the Institute for Healthcare Improvement (IHI) Model for Improvement (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e); the Mayo Evaluation of Reflection on Improvement Tool (MERIT) targets reflective domains (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e); and the Beliefs, Attitudes, Skills, and Confidence in Quality Improvement Scale (BASiC-QI) offers a multidimensional self-assessment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, most tools are anchored in North American paradigms and emphasize limited sets of competencies.\u003c/p\u003e \u003cp\u003eIn Brazil, QI education and practice have been strongly influenced by diagnostic-oriented cycles rooted in Juran\u0026rsquo;s quality improvement (\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This approach foregrounds structured problem diagnosis\u0026mdash;process mapping, cause-and-effect analysis, Pareto analysis, and stratification\u0026mdash;before setting aims and testing changes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). By contrast, the IHI framework promotes a rapid approach starting from aims, measures, and changes, tested through Plan-Do-Study-Act (PDSA) cycles (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Both approaches are widely used; in complex settings such as the SUS, teams frequently need both rigorous diagnostic capability and disciplined testing to achieve reliable change. International models of clinical governance and audit similarly emphasize diagnostic cycles as a core component of quality management (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this landscape, remains a gap for an instrument that (i) integrates competencies from both the diagnostic-oriented tradition and the rapid approach, and (ii) explicitly assess behavioral (non-technical) skills\u0026mdash;such as leadership, communication, and teamwork now recognized as critical to sustaining change (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Addressing this gap may support training, mentoring, and deployment of QI initiatives in resource-constrained, high-turnover environments that are typical of many SUS services.\u003c/p\u003e \u003cp\u003eWe therefore aimed to develop QualiComp, a competency questionnaire for QI in health services, and to gather initial evidence of content validity, acceptability, and structural validity in a student/professional sample.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eWe conducted a multi-step validation study to develop QualiComp and obtain initial evidence of its measurement properties: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) instrument construction grounded in a competency framework; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) expert content assessment using a two-round Delphi technique; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) cognitive pre-testing; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) assessment of structural validity by exploratory factor analysis (EFA) alongside estimation of internal consistency.\u003c/p\u003e \u003cp\u003eThe study was carried out at the Federal University of Rio Grande do Norte (UFRN), Brazil. The recruitment process was conducted in two distinct phases. The first phase, involving the recruitment of experts for the Delphi technique, took place between August 18, 2023, and October 24, 2023. The second phase, comprising the administration of the questionnaire for assessment of structural validity, was carried out from June 26, 2024, to June 25, 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStep 1. Instrument construction\u003c/h3\u003e\n\u003cp\u003eDrawing on the Institute for Healthcare Improvement (IHI) Model for Improvement (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), the Spanish diagnostic-oriented improvement tradition (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and evidence on behavioural (non-technical) skills in QI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), we specified a competency model encompassing three domains:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ebeliefs and attitudes toward QI;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003etechnical-analytical skills (eg. problem diagnosis and use of improvement tools), and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ebehavioural skills (leadership, teamwork, communication).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eItem generation combined this competency model with the authors' teaching and implementation experience in the Brazilian Unified Health System (SUS), as well as the MUSIQ (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and COM-B (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) frameworks, in order to explicitly integrate motivation and capability-related dimensions.\u003c/p\u003e \u003cp\u003eThe initial pool comprised 37 items across four sections: basic knowledge (4 items), attitudes (5 items), technical skills (21 items), behavioural skills (7 items). After content validation and cognitive testing (Steps 2 and 3), a 31-item version was retained for psychometric analyses. The English version of the instrument is available as Supplementary File 1.\u003c/p\u003e\n\u003ch3\u003eStep 2. Content validity (Delphi)\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eParticipants.\u003c/b\u003e We purposively recruited QI experts according to recommended criteria (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e): formal training in QI and more than 5 years of experience in leading improvement projects and/or teaching QI. We invited 20 experts, anticipating non-response rates of 30\u0026ndash;50% in the first round and 20\u0026ndash;30% in the second round.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProcedures.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn Round 1, experts received: (i) the draft instrument (spreadsheet format), (ii) a summary of the underlying competency model, and (iii) detailed instructions for rating. Each item was rated on 5-point Likert scales for relevance, adequacy, usefulness, and clarity, and experts could provide qualitative comments and suggestions for rewording, deletion, or addition of items.\u003c/p\u003e \u003cp\u003eIn Round 2, we re-presented the revised items, highlighting changes (e.g. tracked changes or site-by-side versions) and requested experts to re-rate them using the same four criteria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each criterion, we calculated item-level and scale-level Content Validity Indexes (I-CVI, S-CVI) following Waltz, Strickland, and Lenz (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A priori, items with I-CVI\u0026le; 0.80 were candidates for revision or deletion, whereas an S-CVI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 was considered evidence of adequate overall content validity. Qualitative comments were analysed thematically and adjudicated by the research team, prioritizing theoretical coherence with the competency model and clarity for the target audience.\u003c/p\u003e\n\u003ch3\u003eStep 3. Cognitive pre-testing\u003c/h3\u003e\n\u003cp\u003eWe conducted cognitive interviews with 10 participants who resembled the target population (intentional sampling across students and health professionals). The objective was to assess comprehension of items and instructions, cognitive load, item order and flow, and potential redundancy.\u003c/p\u003e \u003cp\u003eInterviews explored how respondents interpreted each item, how they chose response options, and any difficulties with terminology or format. Based on emerging issues, we planned iterative edits to wording, instructions, and item sequence until no new relevant problems were identified (problem saturation). Completion time for the full instrument was recorded to assess feasibility (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStep 4. Structural validity\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and data collection\u003c/h2\u003e \u003cp\u003eThe 31-item QualiComp was administered to 155 respondents from three groups:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003emedical students enrolled in a mandatory course on quality management in health services;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003estudents from the Master Degree in Quality Management in Health Services (Qualisa\u0026uacute;de/UFRN); and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003estaff from the university hospital.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe questionnaire was administered primarily in digital format (email or application-based link) with paper forms available as backup. All items used a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). All participants provided informed consent prior to participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExploratory Factor Analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted an EFA using Mplus (version 7), under a structural equation modeling framework. Given the ordinal nature of the response scale, we used the robust weighted least squares estimator with mean and variance adjustment (WLSMV), which first estimates the polychoric correlation matrix and then minimizes residuals.\u003c/p\u003e \u003cp\u003eWe inspected the polychoric correlation matrix to identify potential issues of extreme collinearity (r\u0026thinsp;\u0026gt;\u0026thinsp;0.90) and item redundancy. Items with excessive missingness or zero variance were flagged for further consideration.\u003c/p\u003e \u003cp\u003eWe used oblique Geomin rotation, allowing factors to be correlated, in line with the conceptual interdependence among beliefs/attitudes, technical-analytical, and behavioral domains. We tested 1\u0026ndash;5 solutions. Factor retention decisions were based on a combination of:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eresults of parallel analysis (polychoric correlations),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eglobal fit indices (Root Mean Square Error of Approximation - RMSEA, Comparative Fit Index - CFI, Tucker-Lewis Index - TLI, Standardized Root Mean Square Residual - SRMR),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eproportion of explained variance, and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003etheoretical interpretability and parsimony of the factor structure.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFactor loadings were evaluated for statistical significance at the 5% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For interpretation, each item was assigned to the factor on which it exhibited the highest statistically significant loading. Cross-loadings and items with low loadings were examined in terms of both statistical performance and conceptual relevance to decide on retention or exclusion.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was conducted in compliance with the ethical standards of the institutional and national research committees, specifically following Resolutions 466/12 and 510/16 of the Brazilian National Health Council (CNS), which regulate research involving human participants in Brazil. Furthermore, the protocol adhered to the principles of the Declaration of Helsinki (1964) and its subsequent amendments. This study was approved by the Research Ethics Committee of the Federal University of Rio Grande do Norte, Brazil (approval No. 6.099.338), approved on 3 July 2023. Informed consent was obtained from all participants. The study only proceeded after participants had read and signed the consent form, which was provided in either electronic or paper format. As the study did not include minors. Participation was voluntary and had no consequences for academic assessment or employment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eContent Validity\u003c/h2\u003e \u003cp\u003eFourteen experts participated in Round 1 and 12 in Round 2 of the Delphi process. In Round 1, most items met the a priori threshold for relevance (I-CVI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.80); three items were slightly below this cut-off (Items 8, 9, and 26: I-CVI\u0026thinsp;=\u0026thinsp;0.79 each). Based on qualitative feedback, we consolidated Items 13 and 14 due to overlap and refined the wording of several items, including reverse-worded items.\u003c/p\u003e \u003cp\u003eIn Round 2, agreement was high across all four criteria. Only Item 9 remained below the relevance threshold (I-CVI\u0026thinsp;=\u0026thinsp;0.75). Given the overall convergence of ratings, stability of scores between rounds, and the conceptual relevance of Item 9, we decided to close the Delphi after Round 2 and retained Item 9 (with revised wording) for subsequent psychometric testing.\u003c/p\u003e \u003cp\u003eScale-level CVI values were high: 0.96 for relevance, 0.97 for adequacy 0.97, 0.97 for usefulness, and 0.96 for clarity, indicating strong overall content validity. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of the Delphi experts, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows item-level CVI values by criterion and round. Reverse-worded items (Item 8 and 9) were recoded prior to the subsequent analyses.\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\u003eCharacterization of Delphi experts (n\u0026thinsp;=\u0026thinsp;14).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInterviewee profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eUndergraduate background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDentistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHighest degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster's\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctorate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostdoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\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\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.00\u003c/b\u003e\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 \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\u003eContent Validity Indexes (CVI) by item and criteria in rounds 1 and 2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRelevance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUsefulness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eClarity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCVI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCVI2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCVI2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCVI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCVI2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCVI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCVI2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Strategies for evaluating healthcare in my health service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. The role of healthcare professionals as part of a system that affects patient and family outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. The importance of variation and evaluation of the quality of healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Approaches to change healthcare processes for the better\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Continuous quality improvement is an essential part of the daily work of all healthcare professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. I and others can contribute to improving the results of care in my health service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Changing healthcare for the better can lead to greater job satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Quality improvement projects increase the burden on healthcare professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. In my service we always do the right things in health care; the problems are just structural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Identify opportunities for improvement in healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Prioritize the most important opportunities for improvement in my local context\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. Use a consensus method to prioritize an improvement opportunity with my work team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. Analyze the causes of problems on quality of care with the aim of improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. Use tools for analyzing causes (e.g. cause-effect diagram, why technique, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. Map a health care process in order to analyze ways of optimizing it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. Develop a criterion or indicator to measure the quality of health care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. Collecting data on a criterion or indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18. Critically analyze the validity of criteria or indicators designed to measure the quality of health care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19. Set clear targets for improving health care (e.g. SMART, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20. Create a multi-faceted improvement intervention (e.g. steering diagram, affinity diagram, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21. Testing changes on a small scale before promoting their implementation (e.g. PDSA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22. Use tools to monitor the implementation of improvement actions (e.g. Gantt chart, 5W2H, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23. Disseminate the improvements achieved in one service to a group of similar health services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24. Use a quality measure to estimate health service performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25. Apply tools to analyze which criteria are priorities for improving quality (e.g. Pareto diagram)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26. Analyze whether there is an association between two variables to identify causes of poor quality (e.g. scatter diagram, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27. Stratify the results of an indicator to identify causes of poor quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28. Construct a baseline in trend charts or control charts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29. Identify significant improvement by looking at trend graphs or control charts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30. Differentiating whether process data is stable or unstable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31. Selecting a good profile of participants for an improvement team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32. Defining responsibilities within an improvement team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33. Monitoring teamwork during a complete improvement project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34. Managing interpersonal conflicts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35. Activate the intrinsic motivation of team members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36. Encourage team members to speak up, question and propose ideas for quality improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37. Communicate openly with team members to solve problems when an improvement project runs into difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: Item 14 was merged into item 13 after round 1; therefore, it was not re-rated in round 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e - Characterization of Delphi experts (n\u0026thinsp;=\u0026thinsp;14).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - Content Validity Indexes (CVI) by item and criteria in rounds 1 and 2\u003c/p\u003e \u003cp\u003eFollowing content approval, the research team reviewed all qualitative suggestions and consolidated overlapping content. Sections 1 (knowledge about quality improvement) and 2 (attitudes toward quality improvement) were merged, retaining items focused on beliefs and attitudes toward QI. Items 1 and 4, which addressed technical nuances already captured by items in the technical-skills domain, were removed. Items 5 and 6 showed similar content; we retained Item 5 because of clearer wording. Item 9, although below the relevance threshold in Round 2, was maintained due to its conceptual importance and earmarked for empirical testing.\u003c/p\u003e \u003cp\u003eThe resulting version taken forward to cognitive pre-test comprised 31 items organized into three sections: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) beliefs/attitudes about QI, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) technical-analytical skills, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) behavioural (soft) skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCognitive pre-testing\u003c/h2\u003e \u003cp\u003eTen participants completed cognitive interviews. Mean completion time for the full instrument was seven minutes (range: 4\u0026ndash;16 minutes), indicating good feasibility for educational and service contexts. Participants did not report comprehension difficulties or problems related to item order or response options. Only minor editorial standardization (e.g. wording consistency and layout) was applied. No content-related changes to items were required as a result of this step.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural Validity (EFA)\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eModel fit and factor retention\u003c/h2\u003e \u003cp\u003eThe 31-item QualiComp was administered to 155 respondents from the three predefined groups. We estimated a series of one- to five-factor EFA solutions using polychoric correlations, the WLSMV estimator, and oblique Geomin rotation.\u003c/p\u003e \u003cp\u003eParallel analysis based on polychoric correlations supported a three-factor solution, which was also consistent with the underlying competency model. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the global fit indices for models with one to five factors, considering both the full set of items and the solution after removing Item 5.\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\u003eModel fit indices for 1\u0026ndash;5-factor EFA solutions (all items vs. minus Item 5).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;(df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSEA [90%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb. RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eA. All items\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\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1527.293(434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127 [0.121; 0.134]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e952.204(404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.094 [0.086; 0.101]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e686.895(375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073 [0.065; 0.082]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e534.427(347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059 [0.049; 0.069]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438.212(320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049 [0.037; 0.060]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB. Item 5 deleted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1519.30(405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133 [0.126; 0,140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e935.12(376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098 [0.090; 0.106]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e667.77(348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077 [0.068; 0.086]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519.29(321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063 [0.053; 0.073]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420.13(295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052 [0.040; 0.064]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. Estimation\u0026thinsp;=\u0026thinsp;WLSMV; rotation\u0026thinsp;=\u0026thinsp;geomin; data\u0026thinsp;=\u0026thinsp;polychoric correlations; response scale\u0026thinsp;=\u0026thinsp;Likert\u003c/em\u003e (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e); \u003cem\u003en\u0026thinsp;=\u0026thinsp;155; RMSEA 90% confidence interval in brackets; pclose reported as \u0026ldquo;Prob. RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026rdquo;. Bold values indicate the selected factor solution.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the 31-item three-factor model, global fit indices were acceptable (RMSEA\u0026thinsp;=\u0026thinsp;0.073, CFI\u0026thinsp;=\u0026thinsp;0.956, TLI\u0026thinsp;=\u0026thinsp;0.945, SRMR\u0026thinsp;=\u0026thinsp;0.075). After removing Item 5, the three-factor solution showed a very similar fit (RMSEA\u0026thinsp;=\u0026thinsp;0.077, CFI\u0026thinsp;=\u0026thinsp;0.954; TLI\u0026thinsp;=\u0026thinsp;0.943; SRMR\u0026thinsp;=\u0026thinsp;0.075. Four- and five-factor solutions yielded marginal improvements in global fit (e.g., for the five-factor model without Item 5: RMSEA\u0026thinsp;=\u0026thinsp;0.052, CFI\u0026thinsp;=\u0026thinsp;0.982), but at the cost of conceptually weak or sparse factors, increased cross-loadings, and imbalance in the distribution of items across factors.\u003c/p\u003e \u003cp\u003eOn grounds of parsimony, theoretical coherence with the competency model, and interpretability, we retained the three-factor solution with Item 5 removed as the final structural model.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - Model fit indices for 1\u0026ndash;5-factor EFA solutions (all items vs. minus Item 5).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eItem performance and factor loadings\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the factor loadings for the initial three-factor solution including all items. In this model, Item 5 exhibited a negative primary loading (\u0026ndash;0.22 on Factor 3) and instability across factors, suggesting poor conceptual alignment with the emergent structure. Unlike most items, which targeted competencies or attitudes, Item 5 emphasised a perceived adverse effect of QI (increased workload), which likely contributed to its inconsistent performance. Item 6 displayed notable cross-loadings on Factors 1 and 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized factor loadings for the three-factor solution (all items).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.786\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.759\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.662\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.469\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.551\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.870\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.953\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.865\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.876\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.757\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.692\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.644\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.689\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.522\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.622\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.716\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.737\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.748\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.671\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.735\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.726\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.768\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.759\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.665\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.687\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.737\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.824\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.714\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\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\u003eAfter removing Item 5, the three-factor solution (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) exhibited a cleaner and more interpretable pattern of loadings. Cross-loadings were reduced, and Item 6 loaded primarily on Factor 2, aligning with its intended classification as a technical-analytical skill.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardised factor loadings for the three-factor solution (minus item 5).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.788\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.759\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.661\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.469\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.556\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.693\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.874\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.814\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.958\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.867\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.878\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.761\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.697\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.650\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.695\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.528\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.624\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.718\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.736\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.750\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.668\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.732\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.763\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.758\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.663\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.685\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.742\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.825\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.717\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 3\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\u003eThe final configuration was as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 1 \u0026ndash; Beliefs and Attitudes toward Quality Improvement\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eComprised Items 1\u0026ndash;4, all with loadings above 0.66, capturing respondents\u0026rsquo; views on the importance and role of QI in routine professional practice.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 2 \u0026ndash; Technical-Analytical Competencies\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eComprised Items 6\u0026ndash;24, with most loadings near or above 0.70. This factor encompassed skills related to identifying opportunities for improvement, using diagnostic tools (e.g. cause-and-effect diagrams, Pareto analysis, stratification), constructing and interpreting indicators, designing and monitoring interventions, and using time-series or control charts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 3 \u0026ndash; Behavioral (Soft) Skills for Improvement\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eComprised Items 25\u0026ndash;31, with strong and predominantly exclusive loadings (e.g., Item 30: 0.83; Item 31: 0.72). This factor reflected competencies in team selection and coordination, conflict management, activation of intrinsic motivation, psychological safety (speaking up and questioning), and open communication during QI projects.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e - Standardized factor loadings for the three-factor solution (all items).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e - Standardised factor loadings for the three-factor solution (minus item 5).\u003c/p\u003e \u003cp\u003eOverall, the loading pattern was statistically robust and conceptually coherent with the a priori competency model. The statistical consistency of the structure was corroborated by eigenvalues and explained variance: the first three factors accounted for 42.4%, 11.6%, and 8.5% of the total variance, respectively, for cumulative variance explained of 62.5%. Additional factors contributed little and incremental variance and increased interpretive dispersion, reinforcing the choice of a three-factor solution.\u003c/p\u003e \u003cp\u003eRemoving Item 5 improved the overall structure by eliminating a source of conceptual noise, and reducing cross-loadings, thereby sharpening factor boundaries and yielding a more parsimonious and theoretically aligned measurement model for QualiComp.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal\u0026rsquo;s findings\u003c/h2\u003e \u003cp\u003eWe developed QualiComp, a competency questionnaire for QI in health services, and obtained initial evidence of its measurement properties. Content validity indices showed high agreement across criteria, cognitive testing indicated good acceptability (mean completion time\u0026thinsp;~\u0026thinsp;7 minutes, no comprehension issues), and exploratory factor analysis supported a parsimonious three-factor structure aligned with the conceptual model: Beliefs/Attitudes (F1), Technical-Analytical Competencies (F2), and Behavioral/Soft Skills (F3). Although four- and five-factor solutions marginally improved global fit indices, they introduced conceptually weak or sparse factors and increased cross-loadings. We therefore retained the three-factor solution on grounds of parsimony and interpretability. The proportion of explained variance (62.5%) and the cleaner factor structure after removing one misfitting item further support the clarity and coherence of the configuration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and theoretical alignment\u003c/h2\u003e \u003cp\u003eIn complex health systems, both the multidimensional nature of quality and the choice of appropriate measurement tools remain central concerns for delivering high-quality services. Instruments that jointly assess technical capability and behavioral (non-technical) skills, such as leadership, communication, teamwork, and stress management, tend to relate more closely related to performance in academic and service settings. Evidence from systematic reviews of non-technical skills in complex clinical environments supports improvements in team behaviours, leadership, and communication, with tangible effects such as reduced response times in ICU cardiac arrest scenarios (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualiComp was designed to integrate motivational, technical, and behavioral components and, in this study, showed initial structural validity consistent with that intent. This integration is particularly relevant in settings with high staff turnover or frequent role rotation of roles, where competencies must be trained and reassessed at short intervals to approximate quality outcomes in care. In both real and simulated training environments, robust tools are needed to detect gaps and guide target adjustments - whether technical or behavioral - so that team performance can be improved in a systematic and iterative manner.\u003c/p\u003e \u003cp\u003eOur final factor solution coheres with established theory. Factor 1 (Beliefs/Attitudes) captures willingness to improve, recognition of variation, and normalisation of improvement as part of everyday work - elements that map closely to the motivation component in COM-B (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Factor 2 (Technical-Analytical Competencies) consolidates the operational core of QI (prioritising opportunities, cause analysis, process mapping, indicators, control charts, impact evaluation), aligning with the capability component in COM-B and with performance determinants emphasised by MUSIQ at the microsystem level (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Factor 3 (Behavioral/Soft skills) brings together collaborative leadership, communication, teamwork, and conflict management\u0026mdash;competencies repeatedly linked to reliability and safety in complex environments.\u003c/p\u003e \u003cp\u003eAlthough models with four or five factors slightly improved global fit, they fragmented constructs and increased cross-loadings. From a measurement perspective, a model that sacrifices interpretability and practical usability for marginal gains in fit may be less useful for educational and managerial purposes. Retaining a three-factor configuration preserves conceptual coherence and supports the formative use of QualiComp in training and service settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMotivational beliefs as a measurable target\u003c/h2\u003e \u003cp\u003eA distinctive contribution of this study is to explicitly positioning of beliefs and attitudes within a motivational domain, analysed alongside technical and behavioral competencies in the same measurement model. Beliefs frequently observed in practice - such as perceptions that QI increases workload or merely adds \u0026ldquo;more checklists\u0026rdquo; - surfaced in our data and loaded strongly on the motivational domain (loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.66 across four items). Prior studies hint at the same mechanism: with workload complaints undermining engagement in QI (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), and tensions between clinical time, administrative burden, and improvement activities acting as barriers to implementation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Together, these findings support treating motivational beliefs as a measurable and actionable target in QI capacity-building strategies, rather than as an unobserved or implicit background factor.\u003c/p\u003e \u003cp\u003eThe content validity process was also important for legitimacy and applicability. Experts from multiple Brazilian regions and Spanish-speaking contexts (Spain, Mexico) participated, in line with Delphi guidance that recommends diversity of origin and perspective to enhance judgement quality (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This diversity likely contributed to item clarity and relevance across different service realities and organizational cultures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eParsimony and Formative Utility\u003c/h2\u003e \u003cp\u003eAlthough four- and five factor solutions yielded marginal gains in global fit indices, these came at the expense of conceptual coherence and parsimony. We therefore favoured the three-factor configuration to maximise interpretability and practical usability as a formative and managerial tool, consistent with the emphasis on actionable capability and motivation in COM-B and microsystem-focused implementation in MUSIQ (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn QI programmes, simpler and clearly interpretable models reduce burden on teams and facilitate incorporation into routine workflows. This, in turn, supports cycles of training, reassessments, and feedback, thereby enhancing the formative utility of measurement (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In this sense, the primary value of QualiComp is not the distal total score per se, but its capacity to guide continuous improvement through dimension-specific diagnosis and mentoring (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Results can be used to identify which domain\u0026mdash;motivational, technical-analytical, or behavioural\u0026mdash;requires more investment, and to tailor curricula and supervision accordingly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing instruments\u003c/h2\u003e \u003cp\u003eThere is a growing emphasis on non-technical (soft) skills across educational and organizational contexts, accompanied by dedicated assessment tools and systematic reviews (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Evidence also links combinations of technical knowledge with skills in communication, management, emotional intelligence, and confidentiality to better organisational performance in healthcare (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In line with this trend, previous studies have proposed integrating technical and behavioral competencies and have shown their influence on performance across clinical and surgical settings (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). From a training perspective, it is therefore reasonable that the quality of instruction - and ultimately service quality - be monitored with instruments that are sensitive to multiple competency axes rather than single-domain measures.\u003c/p\u003e \u003cp\u003eWithin this landscape, QualiComp advances beyond instruments that traditionally privilege one axis at a time (\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) by integrating three domains in a single measure: attitudes and beliefs, technical-analytical competencies, and behavioural (soft) skills. Compared with tools that focus on narrower targets - such as reflective domains or application of aims/measures/changes - BASiC-QI (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) is among the most innovative we identified for encompassing more than one axis; still, it addresses two domains, whereas QualiComp captures three. This broader scope supports richer formative diagnoses to design training and mentorship pathways and enhances applicability in multiprofessional teams, while maintaining a brief administration time suitable for repeated use in educational and service contexts.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eWe envisage three immediate uses of QualiComp:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFormative diagnosis by dimension, to plan curricula and on-the-job training based on the specific profile of beliefs, technical-analytical skills, and behavioural competencies in a given cohort or service.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMentoring allocation and goal setting by domain, for example emphasising behavioural skills in services with high staff turnover or psychological safety issues or strengthening analytical capability where complex process redesign is required).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLongitudinal monitoring of competencies across QI cycles, enabling programmes to test responsiveness to training, track progress over time, and tailor supports where gaps persist.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn addition to valid instruments for evaluating QI competencies, it is essential to train professionals and managers to use such tools as indicators for designing and adjusting training strategies. The results of QualiComp are most useful when interpreted by teams who can translate dimension-specific findings into concrete educational and organizational interventions.\u003c/p\u003e \u003cp\u003eThe statements and themes included in the instrument allow its application in undergraduate courses, specialist training programs, and collaborative improvement projects, as it covers knowledge and skills addressed in the methodologies widely used in Brazil, including diagnostic-oriented cycles and rapid-cycle improvement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, limitations and future research\u003c/h2\u003e \u003cp\u003eStrengths of this study include explicit theoretical grounding (COM-B; MUSIQ), a transparent content-validation process with experts from multiple regions and countries, appropriate methods for ordinal data (polychorics correlations, WLSMV estimator, oblique rotation), and evidence of operational feasibility (short completion time and good acceptability). The integrated scope across beliefs/attitudes, technical-analytical and behavioural domains is a practical advance for multiprofessional teams working in QI.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, findings derive from a single-context convenience sample and rely on self-report Likert responses, which may limit generalizability and introduce social desirability or common-method variance. Second, cross-sectional design did not allow assessment of temporal stability (test-retest) or responsiveness to training and mentoring interventions. Third, we report initial structural validity based on EFA; confirmatory factor analysis (CFA) in an independent sample is still required. Fourth, we did not examine measurement invariance across subgroups (e.g. profession, sex/gender, experience) or convergent validity with external instruments or objective performance indicators. Finally, data were collected in Portuguese in a Brazilian academic-service setting; generalization to other languages, regions, and service configurations should be made cautiously until cross-cultural adaptation and validation studies are conducted.\u003c/p\u003e \u003cp\u003eFuture research should: (i) conduct CFA in independent, multicentre samples (e.g. undergraduate health courses and frontline services), with detailed fit indices and model refinement; (ii) test measurement invariance (configural, metric, scalar) across profession, sex/gender, and experience strata; (iii) extend evidence on reliability, including ω and α with confidence intervals and test\u0026ndash;retest reliability over 2\u0026ndash;4 weeks; (iv) examine responsiveness to training and mentoring programmes using pre-post and repeated measures designs; (v) investigate convergent and criterion-related validity through associations with external measures (e.g. BASiC-QI) and, when feasible, objective process or performance indicators; (vi) assess ceiling and floor effects and potential differential item functioning (DIF); (vii) explore item response theory (IRT) models to refine item hierarchy and information; and (viii) develop interpretation norms or cut-points by context (teaching vs. service, primary vs. hospital care) to support formative decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eQualiComp provides an integrated, practical perspective on QI competencies, capturing Beliefs/Attitudes, Technical-Analytical skills, and Behavioral/Soft skills in a single, brief instrument. This study offers initial evidence of content validity, acceptability, and structural validity, supporting its use as a formative tool to diagnose needs, guide training and mentorship, and monitor competency development across QI cycles.\u003c/p\u003e \u003cp\u003eFurther confirmatory studies and longitudinal studies are needed to consolidate its measurement properties, including reliability, invariance, and responsiveness. In parallel, careful implementation and interpretation in educational and service settings may help QualiComp contribute to strengthening QI capacity and, ultimately, to improving the quality and safety of care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to The Federal University of Rio Grande do Norte for supporting the graduate program in improving the quality of health services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The study was supported by the authors' host institutions as part of their standard research activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations. The study was conducted in compliance with the ethical standards of the institutional and national research committees, specifically following Resolutions 466/12 and 510/16 of the Brazilian National Health Council (CNS), which regulate research involving human participants in Brazil. Furthermore, the protocol adhered to the principles of the Declaration of Helsinki (1964) and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003eEthical approval was formally granted by the Research Ethics Committee of the Onofre Lopes University Hospital at the Federal University of Rio Grande do Norte (HUOL-UFRN), under protocol number 6.099.338.\u003c/p\u003e\n\u003cp\u003eParticipation was strictly voluntary, and all individuals were informed of their right to withdraw from the study at any stage without penalty. \u0026nbsp;Informed consent was obtained from all participants prior to data collection, utilizing either electronic or paper-based formats as appropriate. All collected data were handled with strict confidentiality and anonymity to protect the privacy of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.P.S, S.S.B,A.L.B.L, Z.A.S.G These authors contributed in the conception, supervision of the study, collection and analysis of data, statistical analysis, critical review and writing of the manuscript.A.C.P.A.S, P.J.M, M.R.F These authors contributed to the collection of data, interpretation, critical review and writing of the manuscript.All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe anonymized dataset supporting the findings of this study has been deposited in the Figshare repository. It can be accessed via the following DOI/Link: https://doi.org/10.6084/m9.figshare.30889727\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization, Organisation for Economic Co-operation and Development, International Bank for Reconstruction and Development. Delivering quality health services: a global imperative for universal health coverage. Geneva: World Health Organization. 2018. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/272465\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/272465\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKruk M, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-Dewan S, Adeyi O, Barker P, Daelmans B, Doubova SV. 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BMJ open. 2018;8(4):e020930.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHibbert PD, Basedow M, Braithwaite J, Wiles LK, Clay-Williams R, Padbury R. How to sustainably build capacity in quality improvement within a healthcare organisation: a deep-dive, focused qualitative analysis. BMC Health Serv Res. 2021;21(1):588.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira RD, Alvim NA. T\u0026eacute;cnica Delphi no di\u0026aacute;logo com enfermeiros sobre a acupuntura como proposta de interven\u0026ccedil;\u0026atilde;o de enfermagem. Escola Anna Nery. 2015;19(1):174\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStetler CB, Legro MW, Wallace CM, Bowman C, Guihan M, Hagedorn H, Kimmel B, Sharp ND, Smith JL. The role of formative evaluation in implementation research and the QUERI experience. 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Validation of a method for assessing resident physicians\u0026rsquo; quality improvement proposals. J Gen Intern Med. 2007;22(9):1330\u0026ndash;4. (QIPAT-7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh MK, Ogrinc G, Cox KR, Dolansky M, Brandt J, Morrison LJ, Harwood B, Petroski G, West A, Headrick LA. The quality improvement knowledge application tool revised (QIKAT-R). Acad Med. 2014;89(10):1386\u0026ndash;91. (QIKAT-R).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteele EM, Butcher R, Carluzzo KL, Watts BV. Development of a tool to assess trainees\u0026rsquo; ability to design and conduct quality improvement projects. Am J Med Qual. 2020;35(2):125\u0026ndash;32. (QIPER).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham C, Johnson-Martinez K, Tomolo A. A Scoring Rubric for the Knowledge Section of the Systems Quality Improvement Training and Assessment Tool. MedEdPORTAL. 2022;18:11290. .(SQITAT).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Care Quality, Quality Improvement, Professional competence","lastPublishedDoi":"10.21203/rs.3.rs-9016562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9016562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSuccessful quality improvement (QI) projects hinge on professionals' competencies to diagnose, implement, and sustain change.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo develop and provide initial validation evidence for QualiComp, a competency questionnaire for QI in health services.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe built a competency model combining widely used approaches in Brazil (diagnostic-oriented cycles) and international QI methods, explicitly integrating attitudes/beliefs, technical-analytical, and behavioral (soft) skills. Content validity was assessed by a two-round Delphi with experts, using item- and scale-level Content Validity Indexes (CVI). Cognitive interviews (n\u0026thinsp;=\u0026thinsp;10) evaluated comprehensibility and response burden. The 31-item prototype was administered to a convenience sample of healthcare students and professionals (n\u0026thinsp;=\u0026thinsp;155). Using polychoric correlations and WLSMV estimator with oblique rotation, we conducted exploratory factor analysis (EFA) to assess structural validity; factor retention combined fit indices, explained variance, and interpretability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eScale-level CVI was high across criteria (relevance 0.96; adequacy 0.97; usefulness 0.97; clarity 0.95). Cognitive interviews indicated good comprehensibility and ~\u0026thinsp;7-minute completion time. EFA supported a parsimonious three-factor solution - beliefs/attitudes (4 items), technical-analytical competencies (19 items), and behavioural skills (7 items) - with adequate fit (e.g., RMSEA 0.077; CFI 0.954; TLI 0.943; SRMR 0.075) and improved structure after removing one misfitting item. Although four- and five-factor models yielded slightly better global fit, they produced conceptually weak/sparse factors and higher cross-loadings; therefore, we retained a parsimonious three-factor structure.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eQualiComp shows promising evidence of content validity, acceptability, and structural validity, offering an integrated view of QI competencies. proving to be a promising and reliable instrument for assessing attitudes, technical, and It can support formative diagnosis, training design, and competency monitoring in QI cycles. Further studies should confirm the structure (CFA), test invariance, reliability, and responsiveness.\u003c/p\u003e","manuscriptTitle":"QualiComp: Development and Initial Validation of a Competency Questionnaire for Quality Improvement in Health Services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 12:52:38","doi":"10.21203/rs.3.rs-9016562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-09T19:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283907551308219865011626387183466737740","date":"2026-03-17T18:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T10:36:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T10:34:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T19:48:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T04:36:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-10T03:55:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e1b6f55-4a89-42ad-9270-95dc6762ec17","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T12:52:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 12:52:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9016562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9016562","identity":"rs-9016562","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}