Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation

Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation | 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 Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation Weiwei Wang, Zhenni Jin, Wenliang Zhang, Yonglin Yang, Xuanning Du, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8400306/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hospital performance evaluation is essential for ensuring accountability, improving service quality, and fostering staff engagement. Yet, existing systems often lack transparency, consistency, and motivational relevance—particularly for administrative and logistical staff whose contributions are frequently misaligned with standardized evaluation criteria. Methods This study employed a three-stage mixed-method design. In Stage One, a 21-item questionnaire was developed to assess three core dimensions: scientific validity, structural fairness, and staff motivation. Hierarchical cluster analysis was used to refine item groupings and validate structural coherence. In Stage Two, 142 administrative and logistical staff completed open-ended surveys. Responses were thematically coded using an artificial intelligence language model to extract high-frequency concepts across dimensions. In Stage Three, a 15-item structured voting questionnaire was created based on the coded themes. Participants selected preferred reform strategies for each domain. Results Cluster analysis confirmed three balanced item clusters, supporting the construct validity of the questionnaire. Thematic analysis revealed five recurring concerns: absence of role-specific indicators, weak feedback mechanisms, inadequate incentives, misalignment between evaluation and job responsibilities, and limited staff participation. In the voting survey, participants showed strong consensus on targeted reforms. For scientific validity, 72% favored linking performance tiers to benefits; 74% supported indicators for innovation and cross-department collaboration. For structural fairness, 74% preferred tenure-adjusted scoring models; 79% endorsed simplified policy communication. For motivation, 81% supported role-specific performance models and contribution-based incentives. The majority of participants endorsed the proposed reform strategies in 14 of the 15 items. Conclusion This study presents a stakeholder-informed, replicable model for optimizing hospital performance evaluation. Through a combination of statistical analysis, artificial intelligence-supported thematic extraction, and structured stakeholder input, we identified actionable gaps and staff-driven solutions. Recommended improvements include developing role-specific key performance indicators, implementing transparent feedback and appeal systems, introducing differentiated incentives, and integrating education and tenure into scoring logic. Institutional transparency and staff participation emerged as critical factors for enhancing credibility and engagement. The proposed multidimensional framework offers practical guidance for improving scientific rigor, structural fairness, and motivational alignment in hospital performance systems. Hospital performance evaluation personnel appraisal organizational fairness staff motivation quality improvement health care administration. Figures Figure 1 Figure 2 Contributions to the literature Current hospital performance research predominantly focuses on clinical staff, often overlooking the unique challenges faced by administrative and logistical personnel who support public health systems. This study introduces a novel, mixed-methods approach that integrates artificial intelligence and stakeholder voting to diagnose specific gaps in evaluation fairness, scientific validity, and staff motivation. The findings provide hospital administrators with a validated, transparent framework to redesign appraisal systems, ensuring they are role-specific and effective at improving workforce engagement and operational efficiency. Background Hospital performance evaluation systems are essential tools for ensuring accountability, improving service quality, and fostering staff development within healthcare organizations. These systems align individual performance with institutional goals and enable data-driven decisions in workforce management, compensation, and strategic planning. However, many existing evaluation models fall short due to a lack of transparency, inconsistent implementation, and limited motivational value. This disconnect is particularly evident among administrative and support personnel, who often perceive these systems as poorly aligned with their actual responsibilities and contributions [ 1 , 2 ]. To address these challenges, hospitals are increasingly seeking performance appraisal models that are not only evidence-based but also reflective of frontline staff experiences. A robust and effective evaluation system must achieve three core objectives: uphold scientific validity, ensure structural fairness, and foster staff motivation. Scientific validity entails designing performance indicators based on objective data and systematic analysis, ensuring that evaluation criteria are both evidence-based and methodologically sound [ 3 ]. Structural fairness emphasizes transparency, impartiality, and the equitable allocation of weights across different roles, responsibilities, and performance metrics [ 4 ]. Staff motivation focuses on setting appropriately challenging goals, providing timely feedback, and incorporating effective incentive mechanisms to promote engagement, growth, and initiative [ 5 ]. This study aims to develop and validate a multidimensional performance evaluation framework centered on these three pillars. Using a mixed-method design, we carried out a three-stage process: first, hierarchical cluster analysis was applied to refine a 21-item evaluation questionnaire; second, an open-ended qualitative survey was conducted to gather in-depth feedback from administrative and logistical hospital staff; and third, a structured voting-style questionnaire was used to prioritize preferred reform strategies. The goal is to create an inclusive, evidence-informed foundation for improving performance evaluation practices in hospital settings. Methods Study Design and Participants This study employed a three-stage, mixed-method design conducted among administrative and logistical staff at a tertiary hospital in Nanjing, China. Participants were recruited using convenience sampling. The study proceeded as follows: Stage One (Questionnaire Optimization): An initial pilot survey was completed by 179 staff members to test the instrument's structure. Based on the results, a revised formal survey was administered to a separate group of 122 staff members to finalize the 21-item scale. Stage Two (Qualitative Survey): A purposive sample of 43 staff members from the formal survey group (n = 122) also participated in an open-ended survey to provide in-depth qualitative feedback. Stage Three (Voting Survey): All participants from the formal survey and qualitative groups (n = 122 and n = 43, totaling 165 individuals) were invited to participate in a final voting survey. A total of 142 staff members responded, yielding a response rate of 86%, to help prioritize reform strategies. Stage One: Questionnaire Optimization The study began with the development of an initial 16-item performance evaluation questionnaire for administrative and logistical hospital staff, informed by expert consultation, literature review, and contextual analysis. This instrument was designed to capture three core domains: scientific validity, structural fairness, and staff motivation. A pilot survey was administered to 179 staff members to test the instrument's preliminary structure, with hierarchical cluster analysis used to assess the latent structure of the pilot data. Based on the pilot findings and expert feedback, the instrument was expanded to 29 items (28 closed-ended and one open-ended). This revised questionnaire was administered in a formal survey to a separate group of 122 administrative staff members. Data from the formal survey underwent a rigorous refinement process to ensure the final scale's reliability and validity. First, hierarchical cluster analysis (SPSS 20.0; average linkage, squared Euclidean distance) was used to assess the conceptual coherence of the item groupings. To further improve internal consistency, three statistical filtering techniques were applied. Items were screened using: (1) standard deviation to identify and remove items with low variance; (2) correlation analysis to assess inter-item coherence; and (3) Cronbach's alpha to measure the internal consistency of the overall scale and each of its three domains. This multi-step process yielded the final 21-item instrument used in the subsequent study phases ( see Supplementary File 1 ). Stage Two: Qualitative Survey An open-ended survey was conducted among 43 staff, with prompts tailored to the three evaluation domains. Responses were analyzed using ChatGPT-4o to identify high-frequency keywords and thematic codes. The use of AI allowed rapid, consistent thematic clustering. Human coders verified results, supporting prior findings on AI-assisted reliability in health research [ 8 ]. Stage Three: Voting Survey A structured 15-item voting-style survey (plus one open-ended item) was distributed to 142 staff participants from earlier stages. Each item offered 2–3 reform options derived from thematic analysis. Responses were aligned to the three framework dimensions, enabling prioritization of feasible reform strategies. This phase bridged qualitative insight with actionable decision-making and ensured stakeholder alignment. Results Stage One: Questionnaire Optimization Result Pilot Survey Findings A total of 179 administrative staff completed the initial 16-item performance evaluation questionnaire, achieving a 100% response rate. The questionnaire assessed awareness of performance policy, implementation satisfaction, and perceptions of feedback mechanisms. Hierarchical cluster analysis (SPSS 20.0; squared Euclidean distance, average linkage) revealed three item clusters: Items 1–12 aligned with scientific validity, Items 13–14 with structural fairness, and Items 15–16 with staff motivation. This structure supported the framework’s conceptual validity. Results highlighted the influence of policy transparency, participation level, and use of evaluation outcomes, guiding subsequent refinement. Formal Survey and Questionnaire Refinement The formal survey was completed by 122 administrative staff, whose demographic characteristics are summarized in Table 1 . Data from this survey were used for the final questionnaire refinement. An initial hierarchical cluster analysis of the 29-item instrument revealed that three items—Q1 (policy awareness), Q20, and Q21 (institutional benchmarking)—were conceptually distant from the core structure and were excluded from further dimensional clustering. The remaining 25 items were grouped into three conceptually coherent dimensions: scientific validity, structural fairness, and staff motivation, as detailed in Table 2 and Figure 1 . Following the application of statistical reliability filters, four additional items (Q16, Q18, Q24, and Q26) were removed due to redundancy or low variance. The final 21-item instrument retained seven items for each of the three core dimensions and demonstrated strong internal reliability, with an overall Cronbach's alpha of 0.95. A final confirmatory cluster analysis validated this revised three-cluster structure, as illustrated in Figure 2 . Stage Two: Qualitative Survey Result In the second phase of the study, a total of 43 open-ended responses were collected from administrative and logistical hospital staff. These responses were evenly distributed across the three core dimensions of the evaluation framework: 14 responses addressed scientific validity, 14 structural fairness, and 15 staff motivation. Qualitative analysis was conducted using the ChatGPT-40 language model, which efficiently extracted high-frequency keywords and clustered them into thematic codes. The resulting codes were organized into 13 core themes across the three evaluation dimensions, as summarized in Table 3 . Stage Three: Voting Survey Results In the final phase of the study, 142 administrative and logistical staff members participated in a structured voting survey. The instrument included 15 single-choice questions—each presenting two to three targeted proposals aligned with one of the three evaluation dimensions. Under the scientific validity domain, 71.8% favored a "performance grades plus rights realization" model, and 73.9% endorsed incorporating talent-oriented indicators . In the structural fairness domain, 73.9% favored a tenure-adjusted scoring model, and 78.9% supported regular dissemination of performance policy whitepapers. In the staff motivation domain, 81.0% favored differentiated performance models tailored to specific roles. Across all 15 structured items, Option A received the majority of votes in 14 questions, indicating a strong consensus across staff subgroups. These preferences are summarized in Table 4 . Discussion This study developed a hospital performance evaluation framework tailored to administrative and logistical staff, using a mixed-methods approach combining quantitative structuring, AI-assisted analysis, and stakeholder prioritization. The findings reveal a strong demand for systems that are transparent, fair, and better aligned with staff roles and career progression. Among key concerns was the inadequacy of existing KPIs, which lacked scientific rigor and role relevance. Participants called for performance indicators based on objective metrics like workload, service quality, and team collaboration [ 10 , 11 ]. Feedback systems were also seen as opaque and insufficient, undermining trust. Our results support integrating transparent feedback mechanisms—such as dashboards, review sessions, and appeal channels—to address procedural justice gaps [ 1 , 5 ]. Motivational misalignment was frequently cited. Uniform reward structures were seen as demotivating, echoing earlier frameworks that advocate for tiered incentives linked to both individual and team contributions [ 5 ]. Scoring mechanisms were also perceived as inconsistent, particularly regarding tenure and job complexity. Adjusting for educational background, experience, and functional responsibility would enhance structural fairness. AI (ChatGPT-4o) was used to streamline thematic coding, offering speed and consistency. Its findings were verified by human reviewers, supporting its utility in qualitative healthcare research [ 8 ]. Beyond its methodological contributions, this framework offers a practical roadmap for health administrators and policymakers seeking to drive institutional reform. For hospital managers, the findings provide a clear mandate to move beyond uniform KPIs and implement the role-specific evaluation models that staff strongly endorsed. Reform can be initiated by forming a cross-departmental task force to co-design the tenure-adjusted scoring systems and transparent feedback channels that staff identified as priorities. At a systemic level, regional health authorities could adopt this stakeholder-driven approach as a best-practice model for redesigning performance systems across multiple institutions, thereby promoting standardized yet flexible evaluation policies. Ultimately, by directly addressing sources of dissatisfaction, these evidence-based reforms have the potential to improve workforce retention and morale. A more engaged administrative workforce is foundational to operational efficiency, which in turn supports the quality and timeliness of patient care. Our goal was to establish a context-sensitive yet generalizable evaluation tool suitable for Chinese hospitals. Moreover, international literature highlights the importance of participatory engagement in performance system design [ 13 ] and the application of lean management principles from other industries to improve hospital efficiency and staff alignment [ 14 ]. Conclusions This study presents a stakeholder-informed and replicable model for optimizing hospital performance evaluation. The findings identified five key reform priorities necessary for improving scientific validity, structural fairness, and staff motivation: 1) developing role-specific KPIs; 2) establishing transparent feedback and appeal channels; 3) implementing differentiated, contribution-based incentives; 4) aligning scoring logic with qualifications and tenure; and 5) fostering institutional transparency through staff engagement . Abbreviations AI Artificial Intelligence KPI Key Performance Indicator OA Office Automation SPSS Statistical Package for the Social Sciences Declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Nanjing Women and Children's Healthcare Hospital (Approval No. 2022KY-184). All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. The requirement for informed consent was waived by the Medical Ethics Committee of Nanjing Women and Children's Healthcare Hospital because the study involved anonymous surveys of staff with minimal risk. Consent for publication Not applicable. Funding This work was supported by “Jiangsu Provincial Hospital Association's Research Topic on Hospital Management Innovation” (Grant Number: JSYGY-3-2023-257). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Author Contribution WW proposed the research concept and methodology, conducted data collection and analysis, validated reproducibility, and wrote the initial draft. YZ proposed the research concept, validated reproducibility, oversaw the project planning and execution, provided supervision, and revised the manuscript. ZJ performed data analysis, prepared visualizations, validated reproducibility, and revised the initial draft. WZ participated in programming and software development, and managed the research database. YY provided research materials and instruments, managed the research database, and applied for and secured research funding. XD conducted experiments, collected data, and managed the research database. JF participated in programming and software development, and managed the research database. All authors read and approved the final manuscript. Acknowledgements We would like to thank the participating hospital staff for their time and valuable input throughout all phases of this study. We also acknowledge the expert panel reviewers for their constructive feedback in the questionnaire development stage. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.The questionnaire developed for this study is included in this published article as Supplementary File 1. References Mannion R, Goddard M. Performance measurement and improvement in health care. Appl Health Econ Health Policy. 2002;1(1):13–23. PMID: 14618744. Bevan G, Evans A, Nuti S. Reputations count: why benchmarking performance is improving health care across the world. Health Econ Policy Law. 2019;14(2):141–161. doi:10.1017/S1744133117000561. PMID: 29547363. Donabedian A. The quality of care: how can it be assessed? JAMA. 1988;260(12):1743–8. 10.1001/jama.1988.03410120089033 . Smith PC, Mossialos E, Papanicolas I, Leatherman S. Performance Measurement for Health System Improvement: Experiences, Challenges and Prospects. Cambridge: Cambridge University Press; 2009. Franco LM, Bennett S, Kanfer R. Health sector reform and public sector health worker motivation: a conceptual framework. Soc Sci Med. 2002;54(8):1255–1266. 10.1016/S0277-9536(01)00094-6 . PMID: 11989961. Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken (NJ): Wiley; 2005. Streiner DL, Norman GR, Cairney J. Health Measurement Scales: A Practical Guide to Their Development and Use. 5th ed. Oxford: Oxford University Press; 2015. Mathis WS, Zhao S, Pratt N, Weleff J, De Paoli S. Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods? 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Available from: https://www.who.int/publications/i/item/9789241511131 Tables Table 1: Basic Information of Formal Survey Participants Gender Frequency Percentage Age Group Frequency Percentage Education Level Frequency Percentage Male 24 19.70% 20-30 12 9.80% Below Bachelor’s Degree 4 3.30% Female 98 80.30% 31-40 64 52.50% Bachelor’s Degree 83 68.00% 41-50 34 27.90% Master’s Degree and Above 35 28.70% > 50 12 9.80% Total 122 100.00% 122 100.00% 122 100.00% Professional Title Frequency Percentage Years Worked at Hospital Frequency Percentage Junior 18 14.80% 30 years 9 7.40% Total 122 100.00% 122 100.00% Table 2: Coefficient Table of 25 Formal Survey Questions No. Item Mean Standard Deviation Correlation Coefficient Cronbach's Alpha Retain Scientific Validity 15.6803 6.50653 0.948 2 Is the performance evaluation process reasonable? 1.9508 0.94346 0.879 0.94 Yes 3 Are the performance indicators designed reasonably? 2 0.97912 0.855 0.942 Yes 4 Is the performance cycle design reasonable? (annual cycle) 1.8525 0.94188 0.858 0.941 Yes 5 Is the performance evaluation method reasonable? 1.918 0.86808 0.882 0.94 Yes 6 Is the coefficient design for non-middle-level staff reasonable? 2.0082 0.96634 0.859 0.941 Yes 7 Is the ratio design for key personnel reasonable? 2.082 1.02522 0.87 0.941 Yes 18 Do the performance results reflect the role and status of internal positions in medical, nursing, and technical departments? 1.9508 0.92578 0.805 0.946 No 19 Does the management method have talent attraction potential? 1.918 0.93234 0.857 0.942 Yes Structural Fairness 13.5 5.61322 0.941 8 Is the seniority coefficient design reasonable? 1.8689 0.94432 0.828 0.936 Yes 9 Is the secondary departmental assessment reasonable? 1.8934 0.92538 0.838 0.934 Yes 10 Is the feedback method reasonable? 2.0164 1.03639 0.882 0.93 Yes 11 Can the current method professionally assess the work? 1.9262 0.86405 0.892 0.928 Yes 12 Can the current method objectively assess the work? 1.8607 0.79581 0.846 0.933 Yes 14 Are the results reasonable compared to peers in the same department with similar qualifications? 1.9098 0.96213 0.887 0.929 Yes 15 Are the results reasonable compared to peers in different departments with similar qualifications? 2.0246 0.98302 0.859 0.932 Yes Staff Motivation 18.1393 6.83007 0.958 13 Are the results commensurate with the position? 1.8852 0.84498 0.865 0.953 Yes 16 Are the goals aligned with the hospital’s overall goals? 1.7213 0.7848 0.784 0.957 No 17 Can the results objectively evaluate the work? 1.8525 0.7891 0.89 0.952 Yes 22 Does the management method motivate you to work harder? 1.9344 0.88829 0.876 0.953 Yes 23 Does the management method promote personal growth? 1.8361 0.81677 0.892 0.951 Yes 24 Does the management method help clarify your responsibilities? 1.6803 0.7073 0.834 0.954 No 25 Does the management method make your work feel meaningful? 1.7459 0.71074 0.847 0.954 Yes 26 Does the management method improve clinical service levels? 1.8443 0.853 0.808 0.954 No 27 Does the management method increase teamwork? 1.8361 0.80659 0.849 0.956 Yes 28 Does the management method enhance satisfaction and sense of belonging? 1.8033 0.79917 0.888 0.958 Yes Table 3: Thematic Analysis of Performance Evaluation Survey Feedback Theme Category Theme Name Typical Keywords Concept Summary Scientific Validity Incentives & Fairness application of evaluation results, title-based coefficients, key personnel ratio Concerns about how evaluation results link to rewards and ensuring fairness via scientific coefficient design. Talent Attraction & Development rewards to attract talent The system should not only assess past performance but also motivate and attract future talent. Cycle & Frequency 1.5 years, quarterly evaluation Some suggest the evaluation frequency should reflect the nature of academic or research work. Data & Transparency transparency, data-driven evaluation, reduce bias Consensus on improving transparency and reducing subjectivity with objective, quantifiable, open processes. Others/Unclar satisfaction, support for research staff, flexible benefits, difficult positions Unclassified but valuable feedback that may indicate emerging themes not yet identified. Structural Fairness Seniority & Education Coefficients postdoc counted as experience, new vs. senior staff unfair, deduction for tech roles Dissatisfaction with current coefficients; distinctions between new hires and experienced/postdoc staff are lacking. Feedback & Appeals Mechanism no feedback channel, suggestion to add OA process A desire for formal feedback and appeal processes to voice disagreement with evaluations. Professional & Objective Evaluation department heads score, tiered scoring, visualization tools Calls for more scientific and fair scoring systems, such as tiered scoring and tool-assisted assessment. Cross-Department Comparability job function differs, not comparable, reference tiered scoring Roles differ significantly, making comparisons unfair; structured models may help resolve this. Awareness of Evaluation System unaware of secondary evaluation, need training for internal staff Lack of knowledge about the system highlights the need for better communication and training. Staff Motivation Post Matching & Appropriateness commensurate, not matching, KPI tailored to job role Respondents stressed role-specific relevance and criticized the one-size-fits-all system. Indicator Differentiation measurable workload, lack of quantification Advocated for quantifiable, differentiated indicators that reflect varied roles and outputs. Incentive Mechanisms set clear targets, no motivation, encourage effort Current methods lack motivational power; suggestions include setting clearer, inspiring targets. Goal Orientation & Growth promote personal growth, structured assessment mechanism Performance evaluations should link to personal development and goal achievement. Teamwork & Contribution everyone shares equally, evaluate by contribution, unfair gain Current models fail to reflect individual contributions in teams; suggest weighting by contribution. Satisfaction & Sense of Belonging not satisfied, satisfaction, belonging Dissatisfaction is tied to lack of fairness and transparency, which undermines belonging. Table 4: Summary of Staff Preferences on Performance Evaluation Optimization Strategies Theme Question Summary Option A Label Option A % Option B Label Option B % Other (%) Scientific Validity Preferred way to improve performance incentives & fairness A. Performance grades + rights realization 71.83% B. Flexible coefficient model 25.35% 2.82% Preferred way to enhance talent attraction & development A. Add talent-indicative indicators 73.94% B. Special trial evaluation for new hires 22.54% 3.52% Preferred method to optimize evaluation cycle & frequency A. Post-based cycle table 50.70% B. Basic cycle + periodic feedback 46.48% 2.82% Preferred method to improve data transparency in evaluation A. Visualization dashboard 67.61% B. Open process & scoring criteria 30.28% 2.11% Structural Fairness Preferred way to refine seniority & education coefficient design A. Seniority conversion model 73.94% B. Unified starting coefficient 23.94% 2.11% Preferred way to improve feedback & appeal mechanisms A. Appeal module in OA 61.97% B. Fixed feedback period after evaluation 35.92% 2.11% Preferred method for professional & objective assessment A. Scoring handbook 65.49% B. Paired scoring + review 31.69% 2.82% Preferred approach to improve inter-department comparability A. Standardized + structured comparison 76.76% B. Functional template by role 20.42% 2.82% Preferred method to enhance understanding of evaluation system A. Push system white paper & flowchart 78.87% B. Q&A week for system interpretation 17.61% 3.52% Staff Motivation Preferred method to improve job-performance alignment A. Job-responsibility match indicator library 76.76% B. Self-evaluation for fit level 20.42% 2.82% Preferred way to differentiate evaluation indicators A. Job category-based models 80.99% B. Annual frontline survey 14.79% 4.23% Preferred performance incentive method A. Quarterly incentive plan 77.46% B. Talent reserve for top performers 19.01% 3.52% Preferred goal-setting & growth tracking approach A. Growth-oriented goal module 77.46% B. Growth tracking review 19.72% 2.82% Preferred way to evaluate teamwork & contribution A. Internal team peer evaluation 59.15% B. Collaboration award system 38.03% 2.82% Preferred way to improve satisfaction & sense of belonging A. Satisfaction survey annually 78.87% B. One-on-one feedback conversation 17.61% 3.52% Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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11:57:15","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95763,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8400306/v1/c0343e64884cacc51d89638a.html"},{"id":100400939,"identity":"b9c6de34-9ae7-4c07-8ebc-b5502098b522","added_by":"auto","created_at":"2026-01-16 11:58:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":395737,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of Initial Item Clustering from Formal Survey\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8400306/v1/9de38614ff7e73c737a1c93f.jpeg"},{"id":100401000,"identity":"c05c5b1d-4d73-460b-bd89-5912042dae85","added_by":"auto","created_at":"2026-01-16 11:58:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36769,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of Final 21-Item Instrument after Refinement\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8400306/v1/4ec441da21d389997804fcd5.png"},{"id":107397854,"identity":"20fed509-24a8-4378-9c1c-82a5807622f5","added_by":"auto","created_at":"2026-04-21 06:57:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8400306/v1/49080ea9-7475-4bf1-aa67-8ff1b6056d71.pdf"},{"id":100400604,"identity":"23cc963d-3e3f-4d0d-8694-1e32458e6594","added_by":"auto","created_at":"2026-01-16 11:58:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7699,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8400306/v1/13a9b795eb48b69ec0190dbc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eCurrent hospital performance research predominantly focuses on clinical staff, often overlooking the unique challenges faced by administrative and logistical personnel who support public health systems.\u003c/li\u003e\n \u003cli\u003eThis study introduces a novel, mixed-methods approach that integrates artificial intelligence and stakeholder voting to diagnose specific gaps in evaluation fairness, scientific validity, and staff motivation.\u003c/li\u003e\n \u003cli\u003eThe findings provide hospital administrators with a validated, transparent framework to redesign appraisal systems, ensuring they are role-specific and effective at improving workforce engagement and operational efficiency.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eHospital performance evaluation systems are essential tools for ensuring accountability, improving service quality, and fostering staff development within healthcare organizations. These systems align individual performance with institutional goals and enable data-driven decisions in workforce management, compensation, and strategic planning. However, many existing evaluation models fall short due to a lack of transparency, inconsistent implementation, and limited motivational value. This disconnect is particularly evident among administrative and support personnel, who often perceive these systems as poorly aligned with their actual responsibilities and contributions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address these challenges, hospitals are increasingly seeking performance appraisal models that are not only evidence-based but also reflective of frontline staff experiences. A robust and effective evaluation system must achieve three core objectives: uphold scientific validity, ensure structural fairness, and foster staff motivation. Scientific validity entails designing performance indicators based on objective data and systematic analysis, ensuring that evaluation criteria are both evidence-based and methodologically sound [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Structural fairness emphasizes transparency, impartiality, and the equitable allocation of weights across different roles, responsibilities, and performance metrics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Staff motivation focuses on setting appropriately challenging goals, providing timely feedback, and incorporating effective incentive mechanisms to promote engagement, growth, and initiative [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to develop and validate a multidimensional performance evaluation framework centered on these three pillars. Using a mixed-method design, we carried out a three-stage process: first, hierarchical cluster analysis was applied to refine a 21-item evaluation questionnaire; second, an open-ended qualitative survey was conducted to gather in-depth feedback from administrative and logistical hospital staff; and third, a structured voting-style questionnaire was used to prioritize preferred reform strategies. The goal is to create an inclusive, evidence-informed foundation for improving performance evaluation practices in hospital settings.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eStudy Design and Participants\u003c/p\u003e \u003cp\u003eThis study employed a three-stage, mixed-method design conducted among administrative and logistical staff at a tertiary hospital in Nanjing, China. Participants were recruited using convenience sampling.\u003c/p\u003e \u003cp\u003eThe study proceeded as follows:\u003c/p\u003e \u003cp\u003eStage One (Questionnaire Optimization): An initial pilot survey was completed by 179 staff members to test the instrument's structure. Based on the results, a revised formal survey was administered to a separate group of 122 staff members to finalize the 21-item scale.\u003c/p\u003e \u003cp\u003eStage Two (Qualitative Survey): A purposive sample of 43 staff members from the formal survey group (n\u0026thinsp;=\u0026thinsp;122) also participated in an open-ended survey to provide in-depth qualitative feedback.\u003c/p\u003e \u003cp\u003eStage Three (Voting Survey): All participants from the formal survey and qualitative groups (n\u0026thinsp;=\u0026thinsp;122 and n\u0026thinsp;=\u0026thinsp;43, totaling 165 individuals) were invited to participate in a final voting survey. A total of 142 staff members responded, yielding a response rate of 86%, to help prioritize reform strategies.\u003c/p\u003e \u003cp\u003eStage One: Questionnaire Optimization\u003c/p\u003e \u003cp\u003eThe study began with the development of an initial 16-item performance evaluation questionnaire for administrative and logistical hospital staff, informed by expert consultation, literature review, and contextual analysis. This instrument was designed to capture three core domains: scientific validity, structural fairness, and staff motivation. A pilot survey was administered to 179 staff members to test the instrument's preliminary structure, with hierarchical cluster analysis used to assess the latent structure of the pilot data.\u003c/p\u003e \u003cp\u003eBased on the pilot findings and expert feedback, the instrument was expanded to 29 items (28 closed-ended and one open-ended). This revised questionnaire was administered in a formal survey to a separate group of 122 administrative staff members.\u003c/p\u003e \u003cp\u003eData from the formal survey underwent a rigorous refinement process to ensure the final scale's reliability and validity. First, hierarchical cluster analysis (SPSS 20.0; average linkage, squared Euclidean distance) was used to assess the conceptual coherence of the item groupings. To further improve internal consistency, three statistical filtering techniques were applied. Items were screened using: (1) standard deviation to identify and remove items with low variance; (2) correlation analysis to assess inter-item coherence; and (3) Cronbach's alpha to measure the internal consistency of the overall scale and each of its three domains. This multi-step process yielded the final 21-item instrument used in the subsequent study phases (\u003cb\u003esee Supplementary File 1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eStage Two: Qualitative Survey\u003c/p\u003e \u003cp\u003eAn open-ended survey was conducted among 43 staff, with prompts tailored to the three evaluation domains. Responses were analyzed using ChatGPT-4o to identify high-frequency keywords and thematic codes. The use of AI allowed rapid, consistent thematic clustering. Human coders verified results, supporting prior findings on AI-assisted reliability in health research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStage Three: Voting Survey\u003c/p\u003e \u003cp\u003eA structured 15-item voting-style survey (plus one open-ended item) was distributed to 142 staff participants from earlier stages. Each item offered 2\u0026ndash;3 reform options derived from thematic analysis. Responses were aligned to the three framework dimensions, enabling prioritization of feasible reform strategies. This phase bridged qualitative insight with actionable decision-making and ensured stakeholder alignment.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStage One: Questionnaire Optimization Result\u003c/h2\u003e\n\u003ch3\u003ePilot Survey Findings\u003c/h3\u003e\n\u003cp\u003eA total of 179 administrative staff completed the initial 16-item performance evaluation questionnaire, achieving a 100% response rate. The questionnaire assessed awareness of performance policy, implementation satisfaction, and perceptions of feedback mechanisms. Hierarchical cluster analysis (SPSS 20.0; squared Euclidean distance, average linkage) revealed three item clusters: Items 1–12 aligned with scientific validity, Items 13–14 with structural fairness, and Items 15–16 with staff motivation. This structure supported the framework’s conceptual validity. Results highlighted the influence of policy transparency, participation level, and use of evaluation outcomes, guiding subsequent refinement.\u003c/p\u003e\n\u003ch3\u003eFormal Survey and Questionnaire Refinement\u003c/h3\u003e\n\u003cp\u003eThe formal survey was completed by 122 administrative staff, whose demographic characteristics are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. Data from this survey were used for the final questionnaire refinement.\u003c/p\u003e\n\u003cp\u003eAn initial hierarchical cluster analysis of the 29-item instrument revealed that three items—Q1 (policy awareness), Q20, and Q21 (institutional benchmarking)—were conceptually distant from the core structure and were excluded from further dimensional clustering. The remaining 25 items were grouped into three conceptually coherent dimensions: scientific validity, structural fairness, and staff motivation, as detailed in \u003cstrong\u003eTable 2 and Figure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFollowing the application of statistical reliability filters, four additional items (Q16, Q18, Q24, and Q26) were removed due to redundancy or low variance. The final 21-item instrument retained seven items for each of the three core dimensions and demonstrated strong internal reliability, with an overall Cronbach's alpha of 0.95. A final confirmatory cluster analysis validated this revised three-cluster structure, as illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eStage Two: Qualitative Survey Result\u003c/h2\u003e\n\u003cp\u003eIn the second phase of the study, a total of 43 open-ended responses were collected from administrative and logistical hospital staff. These responses were evenly distributed across the three core dimensions of the evaluation framework: 14 responses addressed scientific validity, 14 structural fairness, and 15 staff motivation. Qualitative analysis was conducted using the ChatGPT-40 language model, which efficiently extracted high-frequency keywords and clustered them into thematic codes. The resulting codes were organized into 13 core themes across the three evaluation dimensions, as summarized in \u003cstrong\u003eTable 3\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eStage Three: Voting Survey Results\u003c/h2\u003e\n\u003cp\u003eIn the final phase of the study, 142 administrative and logistical staff members participated in a structured voting survey. The instrument included 15 single-choice questions—each presenting two to three targeted proposals aligned with one of the three evaluation dimensions. Under the scientific validity domain, 71.8% favored a \"performance grades plus rights realization\" model, and 73.9% endorsed incorporating talent-oriented indicators . In the structural fairness domain, 73.9% favored a tenure-adjusted scoring model, and 78.9% supported regular dissemination of performance policy whitepapers. In the staff motivation domain, 81.0% favored differentiated performance models tailored to specific roles. Across all 15 structured items, Option A received the majority of votes in 14 questions, indicating a strong consensus across staff subgroups. These preferences are summarized in \u003cstrong\u003eTable 4\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed a hospital performance evaluation framework tailored to administrative and logistical staff, using a mixed-methods approach combining quantitative structuring, AI-assisted analysis, and stakeholder prioritization. The findings reveal a strong demand for systems that are transparent, fair, and better aligned with staff roles and career progression.\u003c/p\u003e \u003cp\u003eAmong key concerns was the inadequacy of existing KPIs, which lacked scientific rigor and role relevance. Participants called for performance indicators based on objective metrics like workload, service quality, and team collaboration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Feedback systems were also seen as opaque and insufficient, undermining trust. Our results support integrating transparent feedback mechanisms\u0026mdash;such as dashboards, review sessions, and appeal channels\u0026mdash;to address procedural justice gaps [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMotivational misalignment was frequently cited. Uniform reward structures were seen as demotivating, echoing earlier frameworks that advocate for tiered incentives linked to both individual and team contributions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Scoring mechanisms were also perceived as inconsistent, particularly regarding tenure and job complexity. Adjusting for educational background, experience, and functional responsibility would enhance structural fairness.\u003c/p\u003e \u003cp\u003eAI (ChatGPT-4o) was used to streamline thematic coding, offering speed and consistency. Its findings were verified by human reviewers, supporting its utility in qualitative healthcare research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond its methodological contributions, this framework offers a practical roadmap for health administrators and policymakers seeking to drive institutional reform. For hospital managers, the findings provide a clear mandate to move beyond uniform KPIs and implement the role-specific evaluation models that staff strongly endorsed. Reform can be initiated by forming a cross-departmental task force to co-design the tenure-adjusted scoring systems and transparent feedback channels that staff identified as priorities. At a systemic level, regional health authorities could adopt this stakeholder-driven approach as a best-practice model for redesigning performance systems across multiple institutions, thereby promoting standardized yet flexible evaluation policies. Ultimately, by directly addressing sources of dissatisfaction, these evidence-based reforms have the potential to improve workforce retention and morale. A more engaged administrative workforce is foundational to operational efficiency, which in turn supports the quality and timeliness of patient care.\u003c/p\u003e \u003cp\u003eOur goal was to establish a context-sensitive yet generalizable evaluation tool suitable for Chinese hospitals. Moreover, international literature highlights the importance of participatory engagement in performance system design [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and the application of lean management principles from other industries to improve hospital efficiency and staff alignment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study presents a stakeholder-informed and replicable model for optimizing hospital performance evaluation. The findings identified five key reform priorities necessary for improving scientific validity, structural fairness, and staff motivation: 1) developing role-specific KPIs; 2) establishing transparent feedback and appeal channels; 3) implementing differentiated, contribution-based incentives; 4) aligning scoring logic with qualifications and tenure; and 5) fostering institutional transparency through staff engagement .\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKey Performance Indicator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOffice Automation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Medical Ethics Committee of Nanjing Women and Children's Healthcare Hospital (Approval No. 2022KY-184). All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. The requirement for informed consent was waived by the Medical Ethics Committee of Nanjing Women and Children's Healthcare Hospital because the study involved anonymous surveys of staff with minimal risk.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by \u0026ldquo;Jiangsu Provincial Hospital Association's Research Topic on Hospital Management Innovation\u0026rdquo; (Grant Number: JSYGY-3-2023-257). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWW proposed the research concept and methodology, conducted data collection and analysis, validated reproducibility, and wrote the initial draft. YZ proposed the research concept, validated reproducibility, oversaw the project planning and execution, provided supervision, and revised the manuscript. ZJ performed data analysis, prepared visualizations, validated reproducibility, and revised the initial draft. WZ participated in programming and software development, and managed the research database. YY provided research materials and instruments, managed the research database, and applied for and secured research funding. XD conducted experiments, collected data, and managed the research database. JF participated in programming and software development, and managed the research database. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank the participating hospital staff for their time and valuable input throughout all phases of this study. We also acknowledge the expert panel reviewers for their constructive feedback in the questionnaire development stage.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.The questionnaire developed for this study is included in this published article as Supplementary File 1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMannion R, Goddard M. Performance measurement and improvement in health care. Appl Health Econ Health Policy. 2002;1(1):13\u0026ndash;23. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789241511131\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789241511131\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Basic Information of Formal Survey Participants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eMale \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e19.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e9.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eBelow Bachelor\u0026rsquo;s Degree \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e80.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e52.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s Degree \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e68.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e27.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s Degree and Above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e28.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026gt; 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e9.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eProfessional Title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003eYears Worked at Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eJunior \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026lt; 5 years \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eIntermediate \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e39.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e21.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAssociate Senior \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e32.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSenior \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e21\u0026ndash;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026gt; 30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Coefficient Table of 25 Formal Survey Questions\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026apos;s Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScientific Validity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e15.6803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.50653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the performance evaluation process reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.9508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.94346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAre the performance indicators designed reasonably?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.97912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the performance cycle design reasonable? (annual cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.94188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the performance evaluation method reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.86808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the coefficient design for non-middle-level staff reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.0082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.96634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the ratio design for key personnel reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.02522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDo the performance results reflect the role and status of internal positions in medical, nursing, and technical departments?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.9508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.92578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method have talent attraction potential?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.93234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural Fairness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.61322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the seniority coefficient design reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.94432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the secondary departmental assessment reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.92538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eIs the feedback method reasonable?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.0164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.03639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCan the current method professionally assess the work?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.9262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.86405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCan the current method objectively assess the work?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.79581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAre the results reasonable compared to peers in the same department with similar qualifications?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.9098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.96213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAre the results reasonable compared to peers in different departments with similar qualifications?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.98302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStaff Motivation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e18.1393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.83007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-IN\"\u003e \u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAre the results commensurate with the position?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.84498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eAre the goals aligned with the hospital\u0026rsquo;s overall goals?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.7213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eCan the results objectively evaluate the work?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method motivate you to work harder?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.9344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.88829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method promote personal growth?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.81677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method help clarify your responsibilities?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.6803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method make your work feel meaningful?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.7459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.71074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method improve clinical service levels?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method increase teamwork?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.80659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 177px;\"\u003e\n \u003cp\u003eDoes the management method enhance satisfaction and sense of belonging?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.8033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.79917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Thematic Analysis of Performance Evaluation Survey Feedback\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypical Keywords\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcept Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 86px;\"\u003e\n \u003cp\u003eScientific Validity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eIncentives \u0026amp; Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eapplication of evaluation results, title-based coefficients, key personnel ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eConcerns about how evaluation results link to rewards and ensuring fairness via scientific coefficient design.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eTalent Attraction \u0026amp; Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003erewards to attract talent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eThe system should not only assess past performance but also motivate and attract future talent.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCycle \u0026amp; Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e1.5 years, quarterly evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eSome suggest the evaluation frequency should reflect the nature of academic or research work.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eData \u0026amp; Transparency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003etransparency, data-driven evaluation, reduce bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eConsensus on improving transparency and reducing subjectivity with objective, quantifiable, open processes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eOthers/Unclar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003esatisfaction, support for research staff, flexible benefits, difficult positions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eUnclassified but valuable feedback that may indicate emerging themes not yet identified.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 86px;\"\u003e\n \u003cp\u003eStructural Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSeniority \u0026amp; Education Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003epostdoc counted as experience, new vs. senior staff unfair, deduction for tech roles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDissatisfaction with current coefficients; distinctions between new hires and experienced/postdoc staff are lacking.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eFeedback \u0026amp; Appeals Mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eno feedback channel, suggestion to add OA process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eA desire for formal feedback and appeal processes to voice disagreement with evaluations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eProfessional \u0026amp; Objective Evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003edepartment heads score, tiered scoring, visualization tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCalls for more scientific and fair scoring systems, such as tiered scoring and tool-assisted assessment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCross-Department Comparability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003ejob function differs, not comparable, reference tiered scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eRoles differ significantly, making comparisons unfair; structured models may help resolve this.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAwareness of Evaluation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eunaware of secondary evaluation, need training for internal staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eLack of knowledge about the system highlights the need for better communication and training.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 86px;\"\u003e\n \u003cp\u003eStaff Motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePost Matching \u0026amp; Appropriateness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003ecommensurate, not matching, KPI tailored to job role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eRespondents stressed role-specific relevance and criticized the one-size-fits-all system.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eIndicator Differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003emeasurable workload, lack of quantification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eAdvocated for quantifiable, differentiated indicators that reflect varied roles and outputs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eIncentive Mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eset clear targets, no motivation, encourage effort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCurrent methods lack motivational power; suggestions include setting clearer, inspiring targets.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eGoal Orientation \u0026amp; Growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003epromote personal growth, structured assessment mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003ePerformance evaluations should link to personal development and goal achievement.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eTeamwork \u0026amp; Contribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003eeveryone shares equally, evaluate by contribution, unfair gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eCurrent models fail to reflect individual contributions in teams; suggest weighting by contribution.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSatisfaction \u0026amp; Sense of Belonging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003enot satisfied, satisfaction, belonging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDissatisfaction is tied to lack of fairness and transparency, which undermines belonging.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4: Summary of Staff Preferences on Performance Evaluation Optimization Strategies\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eQuestion Summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eOption A Label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eOption A %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eOption B Label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eOption B %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eOther (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 85px;\"\u003e\n \u003cp\u003eScientific Validity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to improve performance incentives \u0026amp; fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Performance grades + rights realization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e71.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Flexible coefficient model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e25.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to enhance talent attraction \u0026amp; development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Add talent-indicative indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e73.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Special trial evaluation for new hires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e22.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred method to optimize evaluation cycle \u0026amp; frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Post-based cycle table\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e50.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Basic cycle + periodic feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e46.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred method to improve data transparency in evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Visualization dashboard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e67.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Open process \u0026amp; scoring criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e30.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 85px;\"\u003e\n \u003cp\u003eStructural Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to refine seniority \u0026amp; education coefficient design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Seniority conversion model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e73.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Unified starting coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e23.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to improve feedback \u0026amp; appeal mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Appeal module in OA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e61.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Fixed feedback period after evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e35.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred method for professional \u0026amp; objective assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Scoring handbook\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e65.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Paired scoring + review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e31.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred approach to improve inter-department comparability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Standardized + structured comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e76.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Functional template by role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred method to enhance understanding of evaluation system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Push system white paper \u0026amp; flowchart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e78.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Q\u0026amp;A week for system interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e17.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 85px;\"\u003e\n \u003cp\u003eStaff Motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred method to improve job-performance alignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Job-responsibility match indicator library\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e76.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Self-evaluation for fit level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to differentiate evaluation indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Job category-based models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e80.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Annual frontline survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred performance incentive method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Quarterly incentive plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e77.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Talent reserve for top performers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e19.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred goal-setting \u0026amp; growth tracking approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Growth-oriented goal module\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e77.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Growth tracking review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e19.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to evaluate teamwork \u0026amp; contribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Internal team peer evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e59.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. Collaboration award system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e38.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePreferred way to improve satisfaction \u0026amp; sense of belonging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA. Satisfaction survey annually\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e78.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eB. One-on-one feedback conversation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e17.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hospital performance evaluation, personnel appraisal, organizational fairness, staff motivation, quality improvement, health care administration.","lastPublishedDoi":"10.21203/rs.3.rs-8400306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8400306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHospital performance evaluation is essential for ensuring accountability, improving service quality, and fostering staff engagement. Yet, existing systems often lack transparency, consistency, and motivational relevance\u0026mdash;particularly for administrative and logistical staff whose contributions are frequently misaligned with standardized evaluation criteria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed a three-stage mixed-method design. In Stage One, a 21-item questionnaire was developed to assess three core dimensions: scientific validity, structural fairness, and staff motivation. Hierarchical cluster analysis was used to refine item groupings and validate structural coherence. In Stage Two, 142 administrative and logistical staff completed open-ended surveys. Responses were thematically coded using an artificial intelligence language model to extract high-frequency concepts across dimensions. In Stage Three, a 15-item structured voting questionnaire was created based on the coded themes. Participants selected preferred reform strategies for each domain.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCluster analysis confirmed three balanced item clusters, supporting the construct validity of the questionnaire. Thematic analysis revealed five recurring concerns: absence of role-specific indicators, weak feedback mechanisms, inadequate incentives, misalignment between evaluation and job responsibilities, and limited staff participation. In the voting survey, participants showed strong consensus on targeted reforms. For scientific validity, 72% favored linking performance tiers to benefits; 74% supported indicators for innovation and cross-department collaboration. For structural fairness, 74% preferred tenure-adjusted scoring models; 79% endorsed simplified policy communication. For motivation, 81% supported role-specific performance models and contribution-based incentives. The majority of participants endorsed the proposed reform strategies in 14 of the 15 items.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study presents a stakeholder-informed, replicable model for optimizing hospital performance evaluation. Through a combination of statistical analysis, artificial intelligence-supported thematic extraction, and structured stakeholder input, we identified actionable gaps and staff-driven solutions. Recommended improvements include developing role-specific key performance indicators, implementing transparent feedback and appeal systems, introducing differentiated incentives, and integrating education and tenure into scoring logic. Institutional transparency and staff participation emerged as critical factors for enhancing credibility and engagement. The proposed multidimensional framework offers practical guidance for improving scientific rigor, structural fairness, and motivational alignment in hospital performance systems.\u003c/p\u003e","manuscriptTitle":"Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:57:54","doi":"10.21203/rs.3.rs-8400306/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30689999-687f-4bcf-b288-58bb8b77eca3","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T06:56:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:57:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8400306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8400306","identity":"rs-8400306","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}