Development and validation of a clinical practice guidelines implementability assessment tool (CPG-IAT) based on the COSMIN framework

Development and validation of a clinical practice guidelines implementability assessment tool (CPG-IAT) based on the COSMIN framework | 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 Development and validation of a clinical practice guidelines implementability assessment tool (CPG-IAT) based on the COSMIN framework Dongmei Zhong, Gregory A Aarons, Yaolong Chen, Nan Yang, Wenjun He, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870282/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: We define guideline implementability as the characteristics of the guideline that reflect the extent to which it is likely to be adopted in clinical practice. Improving the intrinsic quality (e.g., context, format, language etc.) of clinical practice guidelines (CPGs) may be a cost-effective and broadly applicable approach. This study was aimed to develop the clinical practice guidelines implementability assessment tool (CPG-IAT) and test its psychometric properties. Methods: The study used the 2022 CPGs recorded in the STAR guideline repository as the evaluation sample. The evaluation team consisted of 60 members with clinical, guideline development, or prior rating experience, responsible for assessing the included guidelines. Guideline evaluation data were randomly assigned to be utilized for an exploratory factor analysis (n=131) or for a confirmatory factor analysis (n=130). Reliability and validity analyses were then conducted with the full sample. Results: The exploratory factor analysis resulted in a 16-item tool with four dimensions representing Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, clinical relevance and actionability. Confirmatory factor analysis supported a priori factor structure. The tool demonstrated excellent internal consistency reliability, convergent validity, construct reliability, split-half reliability, test-retest reliability, inter-rater reliability and pragmatic. Conclusions: The CPG-IAT serves as a good tool for standardizing guideline development prior to their creation and for evaluating guideline implementability after development. The tools developed in this study not only provide a scientific basis for assessing the implementability of CPGs but also offer robust support for future research and practice in related fields. Trial Registration: China Clinical Trails Registry (ChiCTR2400086931); registered July 15, 2024. https://www.chictr.org.cn/ Clinical practice guideline Implementability Assessment Tool Figures Figure 1 Figure 2 Contributions to the Literature This study empirically validates a multidimensional structure of guideline implementability, moving beyond conceptual discussions by identifying four core and measurable domains-Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, Clinical Relevance, and Actionability. By applying rigorous exploratory and confirmatory factor analytic methods, this study demonstrates how theoretically proposed implementability constructs converge and reorganize when examined through empirical data, providing methodological insight for future guideline measurement research. The resulting implementability assessment tool translates abstract implementation principles into operational items, offering guideline developers and users a practical instrument to identify modifiable features that may facilitate guideline uptake in clinical practice. Introduction Clinical practice guidelines (CPGs) are developed through a rigorous process of evidence evaluation with the aim of facilitating the implementation of evidence and standardizing best practices among practitioners [ 1 , 2 ] . The number of guidelines is increasing annually, while low adherence remains a significant challenge [ 3 , 4 ] . Low adherence is due to a variety of constraints that can be categorized as being related to either external environmental factors (e.g., medical personnel, medical institutions, local policies) and/or intrinsic characteristics (e.g., guideline context, format, language etc.), with the former referring to guideline users, and the latter referring to inherent features associated with the guidelines themselves [ 5 , 6 ] . Although both external and intrinsic factors are important when seeking to strengthen guideline implementation, many scholars have argued that a focus on improving the intrinsic quality may be a more cost-effective and broadly applicable approach [ 3 , 7 ] . It is particularly important to identify intrinsic factors, because in many cases they can be ameliorated or fully remedied by guideline authors while the guideline is being developed [ 8 , 9 ] . Shiffman et al. defined a set of characteristics that predict the relative ease of implementation of guideline recommendations as implementability [ 8 ] . We define guideline implementability as the characteristics of the guideline that reflect the extent to which it is likely to be adopted in clinical practice [ 3 ] . More attention is needed in regard to clinical practice guidelines implementability as a key step in effective guideline implementation. In recent decades, tools and frameworks were developed for clinicians and methodology experts to evaluate the implementability of CPGs. Our recent systematic review identified 15 concepts, theories, models, frameworks, or scales related to guideline implementability [ 10 ] , including AGREE (Appraisal of Guidelines, REsearch and Evaluation) [ 11 ] , GLIA (Guideline Implementability Appraisal) [ 8 ] , GUIDE-IT (Guideline Implementability Tool) [ 12 ] , GUIDE-M (Guideline Implementability Decision Excellence Model) [ 13 ] , etc. Based on the results of the systematic review, combined with external consultations and natural language processing tool-assisted, we created a Comprehensive Framework for Guideline Implementability (CFGI) [ 10 ] . Furthermore, Kun Zou et al. developed a generalised tool for evaluating the success of the implementation of clinical practice guidelines (A-GIST) [ 14 ] . However, few existing tools for evaluating guideline implementability have a clear theoretical basis or have a systematic reliability and validity verification [ 5 ] . Given the limitations of existing guideline evaluation tools, guideline implementation can be advanced by integrating new knowledge from implementation science to develop and validate a new guideline implementability evaluation tool underpinned by guideline implementability theory [ 5 ] . Previously, we developed the CFGI, which provides a systematic theoretical basis for the implementability of guidelines [ 10 ] . Through a factorial experiment, the validity of the constructs within the CFGI framework were validated. The present study aimed to develop a clinical practice guidelines implementability assessment tool (CPG-IAT) based on CFGI that has been verified through empirical research, and systematically evaluate its reliability and validity based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist [ 15 , 16 ] . Methods Item generation Item generation and dimension identification proceeded in five phases. First, based on the previously established CFGI framework [ 10 ] , an initial item pool covering six dimensions was developed by integrating evidence from an extensive literature review [ 10 ] . The literature review at this stage focused on articles addressing barriers and facilitators of guideline implementation. Second, rapid qualitative interviews were conducted with 15 guideline stakeholders, including 4 nurses, 3 physicians, 4 organizational managers, and 4 guideline methodologists, to gain a deeper understanding of the attributes and characteristics of guideline implementability. Third, a two-round Delphi process was conducted to screen and refine the items [ 17 , 18 ] . In total, 90 stakeholders from Asia, Africa, Europe, and North America participated, including 38 healthcare professionals, 29 healthcare organization managers, 15 implementation science experts, and 8 guideline methodologists. Fourth, potential items were reviewed by the investigative team and program managers for face validity and content validity. Twenty-six items were developed that represented five potential content dimensions of guidelines implementability: clarity, applicability, reliability, readability, and transparency. Fifth, a pilot survey using the tool to rate 30 CPGs was conducted to estimate the item discrimination, dispersion, correlation coefficients, and internal consistency [ 19 ] . Items with low relevance or poor consistency were removed to generate a pragmatic and optimized version of the tool [ 20 ] . The purpose was to refine items and improve the tool structure based on feedback from experts and guideline users. During the pilot survey, we removed the items with a resolution coefficient < 0.50, a correlation coefficient < 0.40, and a CITC value < 0.20. Finally, the first version of the CPG-IAT with five dimensions and 21 items, were developed. Guidelines and Participants The Scientific, Transparent, and Applicable Rankings (STAR) tool [ 21 ] has applied to evaluate Chinese CPGs and consensus published in 2020, 2021, and 2022 [ 22 – 24 ] . CPGs included in the most recent STAR assessment cycle served as the sampling frame for this study [ 25 ] . As of October 24, 2024, the STAR Working Group had completed evaluations of guidelines published in 2022. To identify eligible guidelines and consensus, the STAR methodological rating group conducted systematic searches on August 24, 2022, and January 15, 2023. Chinese-language searches were performed in the China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, the Chinese Biomedical Literature Database (CBM), and the Chinese Medical Journals Database, while English-language searches were conducted in PubMed and Web of Science. The corresponding search periods were January 1 to July 31, 2022, and January 1 to December 31, 2022. In addition, supplementary searches were carried out in Hong Kong Journals Online, the Hong Kong and Macao Journals Network, the Macao Journal Paper Index, and the Taiwan Journal Paper Index [ 21 ] . The evaluation working group consisted of a Steering Committee, a Secretariat, an Evaluation Panel, and a Quality Control Group. The Steering Committee comprised two members and was responsible for developing the evaluation framework and designing specific assessment procedures, overseeing the evaluation process, and providing methodological consultation and guidance. The Secretariat included two members and was responsible for supporting the Steering Committee in the organization and management of the evaluation activities, as well as for collecting, organizing, and archiving evaluation materials. The Evaluation Panel was composed of 60 individuals with clinical, guideline development, and/or prior rating experience, responsible for assessing the included guidelines. The Quality Control Group consisted of three authors and was responsible for reviewing and approving the evaluation results. Procedure The study was approved by the appropriate Institutional Review Boards prior to participants recruitment and informed consent was obtained prior to administering surveys. Members of the specialty committees within the working group completed an online training course and reviewed training materials on the CPG-IAT. Following the training, participating members underwent an assessment and certification process; only those who passed the certification were eligible to participate in the evaluation. Members of the Evaluation Panel independently assessed each included guideline using the CPG-IAT. Based on the full text of the guideline and supplementary materials, evaluators judged whether each item’s criteria were met, selected an item score on a 5-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree), and documented the rationale for each rating in the evidence form. Upon completion of all evaluations, members of the Quality Control Group, with assistance from the Secretariat, reviewed the submitted materials to check the consistency between scores and supporting rationales, as well as to identify any internal inconsistencies or contradictions. If issues were identified, the materials were returned to the evaluators for revision. After independent evaluations were completed, the Secretariat cross-checked the ratings from two evaluators and calculated the intraclass correlation coefficient (ICC) to assess inter-rater agreement. When the ICC was ≤ 0.75, the guideline was assigned to a third evaluator by the Secretariat, and the ICC was recalculated based on the three sets of ratings. When the ICC exceeded 0.75, the final score for the guideline was calculated as the mean of the three evaluation results [ 26 ] . Statistical analyses Data were randomized to be utilized for either the EFA (exploratory factor analysis) (n = 131) or CFA (confirmatory factor analysis) (n = 130). Exploratory factor analysis was used to derive and evaluate the factor structure of the tool using IBM SPSS. Principal axis factoring was selected for factor extraction because it allows for consideration of both systematic and random error and Promax oblique rotation was utilized for factor rotation as we assumed that derived factors would be correlated [ 27 ] . Item inclusion or exclusion was based on an iterative process in which items with relatively low primary loadings (e.g., 0.30) were removed [ 27 ] . The number of factors to be retained was determined based on parallel analysis, factor loadings, and interpretability of the factor structure as indicated in the rotated solution. Parallel analysis is among the better methods for determining the number of factors based on simulation studies [ 28 ] . Parallel analysis was based on estimation of 1000 random data matrices with values that correspond to the 95th percentile of the distribution of random data eigenvalues [ 29 , 30 ] . The random values were then compared with derived eigenvalues to determine the number of factors. Confirmatory factor analysis was performed to validate the factor structure of the CPG-IAT developed in exploratory factor analysis using AMOS 26.0 (IBM Corp., Armonk, NY, USA). Model fit was assessed using several empirically supported indices: the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). CFI and TLI values greater than 0.90, RMSEA values less than 0.10, and SRMR values less than 0.08 indicate acceptable model fit [ 31 – 34 ] . If necessary, bootstrap Bollen-Stine P would be utilized to assist confirmatory factor analysis when the model fit was not ideal [ 35 , 36 ] . The average variance extracted (AVE) was calculated using the output of the confirmatory factor analysis [ 37 , 38 ] . Internal consistency, construct reliability, Spearman-Brown split-half reliability and test-retest reliability were analysed for the total sample during the formal investigation stage [ 5 ] . Internal consistency was evaluated using Cronbach’s alpha along with McDonald’s omega, with values ≥ 0.70 considered acceptable [ 39 ] . The test-retest reliability was calculated by randomly selecting 50 guidelines from the total sample, and having different evaluators conduct the evaluation two weeks later. The inter-rater reliability was calculated by randomly selecting 2 evaluators to assess 10 guidelines. The psychometric and pragmatic evidence rating scale (PAPERS) was used to evaluate pragmatic of the CPG-IAT, and the evaluation content included normativity, cost of use, language, training cost, and length [ 40 ] . Results The information of guidelines A total of 291 guidelines were included in both the pilot survey and the formal investigation stage. Regarding specialty distribution, the included guidelines covered 30 medical specialties. Oncology had the highest number of included guidelines (n = 36), while pathology, reproductive medicine, and cardiothoracic surgery had the fewest, with one guideline each (see Fig. 1 ). Exploratory factor analysis An iterative approach was taken to conducting the factor analyses and item reduction. In the first iteration and consistent with our hypotheses, five factors were specified and all 21 items were included. The EFA results indicated that only one item met the factor loading criteria for the proposed fifth factor (loading > 0.40 on the primary factor and < 0.30 loading on any secondary factors) [ 27 ] . Factors defined by a single item are considered poorly defined and psychometrically unstable [ 27 ] ; therefore, the fifth factor was not retained. The parallel analysis also suggested a four factor solution. Thus, we conducted the next EFA specifying four factors. The results suggested the removal of 5 items because of low primary factor loadings and/or high cross loadings. Thus, 16 items and 4 factors were retained in the final EFA. Table 1 clinical practice guidelines implementability assessment tool and item statistics Tool, items, and total Mean sd ev v α EFA factor loadings 1 2 3 4 1. Methodological Rigor and Transparency 14.77 7.21 7.67 46.0 0.919 G01. Clearly identifies the intended users and target population 3.10 1.34 0.702 -0.122 0.012 0.085 G02. Clearly describes the criteria for evidence selection 2.34 1.39 0.879 0.002 0.002 -0.014 G03. Evidence has been systematically searched 2.21 1.44 0.916 0.022 -0.077 -0.020 G04. Mentions plans or processes for guideline updating 2.29 1.55 0.604 0.041 0.132 0.113 G05. Provides a detailed description of the process used to develop recommendations 2.37 1.42 0.913 -0.002 -0.041 -0.015 G06. Discloses key steps in the guideline development process 2.46 1.39 0.807 0.080 0.013 -0.056 2. Recommendation Clarity and Interpretability 23.87 5.62 2.11 11.4 0.858 G07. Recommendations in the guideline text are clearly identifiable 3.50 1.43 0.076 0.722 0.047 -0.096 G08. There is a clear linkage between recommendations and supporting evidence 3.98 1.26 0.094 0.622 0.188 0.061 G09. Provides the quality of evidence and strength of recommendation for each item 3.39 1.75 0.326 0.484 -0.042 0.018 G10. The wording of recommendations is clear and concise 4.43 0.86 -0.098 0.767 0.006 0.278 G11. The content is complete and logically organized 4.24 0.91 0.026 0.677 0.201 -0.196 G12. The layout is well-designed and facilitates quick reference and use 4.33 0.87 -0.074 0.870 -0.097 -0.052 3. Clinical Relevance 8.12 2.22 1.23 5.4 0.868 G13. Clearly defines the specific clinical questions 3.97 1.21 0.073 0.056 0.807 -0.026 G14. Recommendations adequately address the clinical questions 4.15 1.16 -0.083 0.067 0.888 0.078 4. Actionability 8.54 1.77 0.96 4.3 0.746 G15. Recommendations provide practical steps for application 4.21 1.05 0.055 -0.277 0.206 0.778 G16. The guideline specifies the implementation settings or applicable contexts 4.34 0.94 0.002 0.244 -0.177 0.800 Tool total 55.31 13.72 0.920 Note: N = 261 for means and standard deviations; n = 131 for other EFA derived statistics; sd= Standard deviation; ev= initial eigenvalue; v= variance accounted for before rotation; α = Cronbach’s alpha; bold font for means and sd indicate the overall tool mean and sd; bold font for EFA factor loadings indicates the tool on which the items load. Table 1 displays the factor means, item means, initial eigenvalues, variance accounted for by each factor, internal consistency reliabilities, and rotated factor loadings. Internal consistencies were high, ranging from 0.746 to 0.919. All items demonstrated acceptable corrected item-total correlations (CITC ≥ 0.30). Deletion of any individual item did not substantially improve Cronbach’s alpha, supporting the retention of all items for subsequent analyses. As shown in Table 2 , factor correlations ranged from 0.14 to 0.64, suggesting that the dimensions represent related but relatively distinct aspects of guideline implementability. Subscale labels were created based on an examination of the items and factor loadings presented in Table 1 . Factor 1 was labeled ‘Methodological Rigor and Transparency’ as it indicated the methodological foundations of guideline development, including transparent definition of target users and populations, explicit evidence selection criteria, and systematic processes for formulating and updating recommendations.Factor 2, ‘Recommendation Clarity and Interpretability’ reflects how recommendations are structured, articulated, and presented to end users, encompassing clarity of recommendation identification, evidence linkage, language, and layout. Factor 3, ‘Clinical Relevance’ represents the extent to which guidelines address clearly defined clinical questions and provide recommendations that directly respond to those questions. Factor 4, ‘Actionability’ indicates whether guideline recommendations offer concrete implementation steps and clearly defined application contexts to support real-word use. The item explanations and examples of the final version of the CPG-IAT are presented in Supplementary 1. Table 2 clinical practice guidelines implementability assessment tool factor intercorrelations Factor 1 2 3 4 1. Methodological Rigor and Transparency 1.00 2. Recommendation Clarity and Interpretability 0.62 1.00 3. Clinical relevance 0.51 0.64 1.00 4. Actionability 0.14 0.42 0.33 1.00 Note: N = 131; All correlations, p < 0.001. Confirmatory factor analysis Confirmatory factor analysis was used with a sample independent of the EFA sample to evaluate the factor structure identified in the EFA. The model was modified using the bootstrap Bollen–Stine P method [ 35 , 36 ] . The factor model demonstrated good fit as indicated by multiple fit indicators (n = 130; CFI = 0.99; TLI = 0.99; RMSEA = 0.03). The SRMR value was 0.11, indicating that the model did not fully reproduce the observed correlation structure. Nevertheless, given the satisfactory performance of incremental fit indices such as CFI, TLI and RMSEA, the overall model fit was considered acceptable. Figure 2 displays the standardized factor loadings for the factor model, ranging from 0.373 to 0.904 and all factor loadings were statistically significant ( p ’s < 0.001). McDonald’s omega coefficients indicated good to excellent internal consistency for all four factors, ranging from 0.74 to 0.92, with Cronbach’s alpha values similarly ranging from 0.746 to 0.920. Convergent validity Confirmatory factor analysis was performed to validate the factor structure obtained from exploratory factor analysis. Convergent validity, expressed as average variance extracted (AVE), ranged from 0.530 to 0.743 across dimensions. Reliability analysis Construct reliability (CR) for each dimension ranged from 0.617 to 0.935, all above the minimum threshold of 0.6. The coefficient of the split-half reliability was 0.810, indicating good split-half reliability. For the 50 guidelines assessed, ICC values ranged from 0.825 to 0.999, all > 0.75, demonstrating good test-retest reliability. Two randomly selected raters independently evaluated 10 guidelines using the tool, yielding a Pearson correlation of 0.794 ( P = 0.006), indicating significant correlation and high inter-rater agreement, confirming good inter-rater reliability. Pragmatic evaluation The tool demonstrated good normativity, showing concentration and distribution characteristics when applied to samples of ≥ 100 guidelines (see Table 1 ). The tool will be freely available to all researchers. It is comprehensible to individuals with at least a bachelor’s degree. Training is required but minimal, indicating acceptable assessor burden. The tool produces quantifiable scores with clear cut-offs, handles missing data via assessor follow-up, and can automatically calculate scores, reflecting minimal workload for evaluators. Finally, the tool contains 16 items, indicating an appropriate length. Discussion The current study describes the development of the measure of guideline implementability for guideline implementation, the CPG-IAT. We used an iterative process to develop items representing guideline implementability and then used quantitative data reduction techniques to develop a brief measure that may be easily and efficiently used for research and applied purposes. The CPG-IAT has the potential to optimize guidelines from within, by providing criteria and evaluation tools for assessing implementability before, during and after the guideline development process. Although we originally proposed five factors of guideline implementability, quantitative analyses supported a four-factor model. Items related to evidence handling, methodological processes, and transparency clustered into a single Methodological Rigor and Transparency factor, while items concerning readability and usability converged into Recommendation Clarity and Interpretability. In contrast, items addressing the definition of clinical questions and the extent to which recommendations address those questions formed a distinct Clinical Relevance factor, and items focusing on implementation steps and applicable contexts were retained as Actionability. This four-factor solution demonstrated acceptable model fit and internal consistency in CFA, offering a more parsimonious and empirically supported representation of guideline implementability while preserving its key conceptual components. Transparency did not emerge as an independent factor because items related to transparency were empirically inseparable from methodological rigor, reflecting that, in practice, transparent reporting of development processes is closely intertwined with perceptions of evidence quality and rigor rather than representing a distinct construct. The CPG-IAT demonstrated strong internal consistency reliability, convergent validity, construct reliability, split-half reliability, test-retest reliability, inter-rater reliability and pragmatic. Given that the CPG-IAT is brief (i.e., 16 items), administration and use in guideline implementability evaluation can be very efficient with little respondent burden. The practicality of this brief scale is consistent with calls for measures that can be utilized in real-world settings where the efficiency of the research process is paramount [ 41 ] . The CPG-IAT shares a common objective with existing implementability appraisal instruments such as GLIA [ 8 ] , GUIDE-IT [ 12 ] , GLAFI (Guideline Language and Format Instrument ) [ 42 ] , and tool developed by Jin et al. [ 43 ] , all of which aim to identify factors that influence the uptake of CPGs in clinical practice. However, the CPG-IAT advances the field in several important ways. First, different with GLIA, which is primarily grounded in Shiffman’s implementability framework and focuses on decidability and executability at the recommendation level [ 8 ] , the CPG-IAT is built upon a more comprehensive theoretical foundation that integrates the CFGI framework, and extensive literature review, and multi-round expert consensus [ 10 ] . Second, different with many previous instruments that have not undergone comprehensive psychometric validation, the CPG-IAT has been systematically tested for reliability, validity, and pragmatic utility across diverse guideline types. In addition, compared with A-GIST [ 14 ] , which evaluates the actual outcomes of guideline implementation based on the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework [ 44 ] , the CPG-IAT focuses on assessing the intrinsic implementability of guidelines before, during, and after development. Together, these two tools form a complementary methodological landscape: the CPG-IAT addresses “how to make guidelines easier to implement,” whereas A-GIST examines “how successfully guidelines have been implemented.” There are several strengths of CPG-IAT. First, the tool is grounded in a comprehensive theoretical framework that integrates conceptual, empirical, and expert-based evidence, ensuring its content is both systematic and aligned with established implementation science principles. Second, the tool has undergone rigorous psychometric testing-including validity, reliability, and pragmatic assessments-ensuring it is both methodologically sound and applicable across diverse types of CPGs. Third, its design supports use throughout the guideline development lifecycle, enabling developers, reviewers, and implementers to identify modifiable factors and target improvements at multiple stages. Together, these strengths position the CPG-IAT as an evidence-based, generalizable, and user-friendly instrument that can effectively guide the development of high-quality, implementable CPGs. Despite the strengths, there are still some limitations. First, although the tool was developed through a rigorous process-including literature review, expert consensus, and psychometric testing-the evaluation relied primarily on CPGs published in 2022 from a single guideline repository. This may limit the generalizability of the findings to guidelines produced in different years, or development systems. Second, the assessment focuses on intrinsic features of guideline documents and development processes rather than contextual or system-level determinants of implementation. As a result, while the tool effectively evaluates document-level implementability, it does not replace organizational- or system-level readiness assessments. Third, although reliability, validity, and pragmatic utility were systematically examined, further testing in real-world implementation projects and across international guideline settings is needed to confirm cross-cultural applicability and predictive validity. Finally, as with most rating tools, the CPG-IAT involves a degree of subjectivity, and inter-rater consistency may vary depending on assessors’ experience and familiarity with guideline methodology. In summary, the CPG-IAT provides a systematically developed and psychometrically validated instrument for assessing the implementability of CPGs at the guideline document level. Although further cross-context validation is needed, the CPG-IAT represents a robust foundation for strengthening guideline quality and supporting future efforts to enhance the translation of evidence into practice. Conclusion A validated CPG-IAT, grounded in the COSMIN framework and informed by the CFGI, has been developed to appraise the intrinsic implementability of CPGs. CPG-IAT provides a structured and balanced assessment of guideline Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, clinical relevance, and actionability, offering a document-level evaluation that precedes real-world implementation. With its solid theoretical foundation, rigorous psychometric validation, and broad applicability across different types of CPGs, the CPG-IAT equips guideline developers, researchers, implementers, and policymakers with a practical tool to identify potential barriers embedded within guideline documents, enhance guideline quality before dissemination, and support the design of strategies to improve future uptake in clinical practice. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) of Southern Medical University (#Southern Medical Audit (2024) No. 012). All the participants signed the informed consent form. Consent for publication Not applicable. Availability of data and material All data generated or analysed during this study are included in this published article and its supplementary information files. Competing Interests The authors declare that they have no competing interest. Funding This project is funded through the following competitive grant: The Swiss Agency for Development and Cooperation (#81067392). Authors’ contributions ZDM conceptualized the paper, assisted with data analysis, developed the study methods, and drafted, reviewed, and edited the manuscript. GAA developed the study methods, reviewed and edited the manuscript. CYL reviewed and edited the manuscript. YN reviewed and edited the manuscript. HWJ reviewed and assisted with data analysis. WR reviewed and edited the manuscript. TJW reviewed and edited the manuscript. WYM assisted data collection. HZZ reviewed the manuscript. XD conceptualized the overall study, obtained funding, and edited and reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgements We acknowledge all members in Acacia Lab for Implementation Science for their cooperation in this study. References Kastner M, Estey E, Bhattacharyya O. 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Evaluation and ranking for scientific, transparent and applicable of Chinese guidelines and consensus published in the medical journals in 2022[J]. National Medical Journal of China, 2023,103(37). STAR（Scientific, Transparent and Applicable Rankings）[EB/OL]. [12-23]. https://www.star-guidelines.cn/index. Koo T K, Li M Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research[J]. J Chiropr Med, 2016,15(2):155-163. Fabrigar L R, Wegener D T, MacCallum R C, et al. Evaluating the use of exploratory factor analysis in psychological research.[J]. Psychological methods, 1999,4(3):272. Zwick W R, Velicer W F. Comparison of five rules for determining the number of components to retain.[J]. Psychological bulletin, 1986,99(3):432. Patil V H, Singh S N, Mishra S, et al. Efficient theory development and factor retention criteria: Abandon the ‘eigenvalue greater than one’criterion[J]. Journal of Business Research, 2008,61(2):162-170. HORN J L. 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Multivariate data analysis[Z]. Taylor & Francis, 2007. Fornell C, Larcker D F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of marketing research, 1981,18(1):39-50. Stensen K, Lydersen S. Internal consistency: from alpha to omega?[J]. Tidsskr Nor Laegeforen, 2022,142(12). Lewis C C, Mettert K D, Stanick C F, et al. The psychometric and pragmatic evidence rating scale (PAPERS) for measure development and evaluation[J]. Implementation Research and Practice, 2021,2:1707064625. Chambers D A, Wang P S, Insel T R. Maximizing efficiency and impact in effectiveness and services research[J]. Gen Hosp Psychiatry, 2010,32(5):453-455. Gupta S, Tang R, Petricca K, et al. The Guideline Language and Format Instrument (GLAFI): development process and international needs assessment survey[J]. Implement Sci, 2022,17(1):47. Yinghui J, Zhihui Z, Xingran H, et al. Development and Validation of Clinical Practice Guideline Implementation Evaluation Tools[J]. Chinese Journal of evidence-based Medicine, 2022,01(22):111-119. Glasgow R E, Vogt T M, Boles S M. Evaluating the public health impact of health promotion interventions: the RE-AIM framework[J]. Am J Public Health, 1999,89(9):1322-1327. Additional Declarations No competing interests reported. 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All factor loadings are standardized and are statistically significant, p\u0026lt;0.001; CFI=0.99; TLI=0.99; RMSEA=0.03; SRMR=0.11. F1 Methodological Rigor and Transparency, F2 Recommendation Clarity and Interpretability, F3 Clinical relevance, F4 Actionability.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8870282/v1/3542655d7ee3f83ff52a429d.png"},{"id":104407986,"identity":"70aaa6ab-285d-4bcd-8b2d-0a28602989e1","added_by":"auto","created_at":"2026-03-11 12:41:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870282/v1/49f2f7f6-1f9b-4936-8452-42cc3392ca9f.pdf"},{"id":103897331,"identity":"45e40d2e-7b07-4b0d-bfde-85a359b82b5a","added_by":"auto","created_at":"2026-03-04 09:11:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30684,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1ItemInterpretationandExamples20260110.docx","url":"https://assets-eu.researchsquare.com/files/rs-8870282/v1/d7474104174008628df1e384.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a clinical practice guidelines implementability assessment tool (CPG-IAT) based on the COSMIN framework","fulltext":[{"header":"Contributions to the Literature","content":"\u003cul\u003e\n \u003cli\u003eThis study empirically validates a multidimensional structure of guideline implementability, moving beyond conceptual discussions by identifying four core and measurable domains-Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, Clinical Relevance, and Actionability.\u003c/li\u003e\n \u003cli\u003eBy applying rigorous exploratory and confirmatory factor analytic methods, this study demonstrates how theoretically proposed implementability constructs converge and reorganize when examined through empirical data, providing methodological insight for future guideline measurement research.\u003c/li\u003e\n \u003cli\u003eThe resulting implementability assessment tool translates abstract implementation principles into operational items, offering guideline developers and users a practical instrument to identify modifiable features that may facilitate guideline uptake in clinical practice.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eClinical practice guidelines (CPGs) are developed through a rigorous process of evidence evaluation with the aim of facilitating the implementation of evidence and standardizing best practices among practitioners\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The number of guidelines is increasing annually, while low adherence remains a significant challenge\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Low adherence is due to a variety of constraints that can be categorized as being related to either external environmental factors (e.g., medical personnel, medical institutions, local policies) and/or intrinsic characteristics (e.g., guideline context, format, language etc.), with the former referring to guideline users, and the latter referring to inherent features associated with the guidelines themselves\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Although both external and intrinsic factors are important when seeking to strengthen guideline implementation, many scholars have argued that a focus on improving the intrinsic quality may be a more cost-effective and broadly applicable approach\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. It is particularly important to identify intrinsic factors, because in many cases they can be ameliorated or fully remedied by guideline authors while the guideline is being developed\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Shiffman et al. defined a set of characteristics that predict the relative ease of implementation of guideline recommendations as implementability\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. We define guideline implementability as the characteristics of the guideline that reflect the extent to which it is likely to be adopted in clinical practice\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. More attention is needed in regard to clinical practice guidelines implementability as a key step in effective guideline implementation.\u003c/p\u003e \u003cp\u003eIn recent decades, tools and frameworks were developed for clinicians and methodology experts to evaluate the implementability of CPGs. Our recent systematic review identified 15 concepts, theories, models, frameworks, or scales related to guideline implementability\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, including AGREE (Appraisal of Guidelines, REsearch and Evaluation)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, GLIA (Guideline Implementability Appraisal)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, GUIDE-IT (Guideline Implementability Tool)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, GUIDE-M (Guideline Implementability Decision Excellence Model)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, etc. Based on the results of the systematic review, combined with external consultations and natural language processing tool-assisted, we created a Comprehensive Framework for Guideline Implementability (CFGI)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, Kun Zou et al. developed a generalised tool for evaluating the success of the implementation of clinical practice guidelines (A-GIST)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, few existing tools for evaluating guideline implementability have a clear theoretical basis or have a systematic reliability and validity verification\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the limitations of existing guideline evaluation tools, guideline implementation can be advanced by integrating new knowledge from implementation science to develop and validate a new guideline implementability evaluation tool underpinned by guideline implementability theory\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Previously, we developed the CFGI, which provides a systematic theoretical basis for the implementability of guidelines\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Through a factorial experiment, the validity of the constructs within the CFGI framework were validated. The present study aimed to develop a clinical practice guidelines implementability assessment tool (CPG-IAT) based on CFGI that has been verified through empirical research, and systematically evaluate its reliability and validity based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eItem generation\u003c/h2\u003e \u003cp\u003eItem generation and dimension identification proceeded in five phases. First, based on the previously established CFGI framework\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, an initial item pool covering six dimensions was developed by integrating evidence from an extensive literature review\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The literature review at this stage focused on articles addressing barriers and facilitators of guideline implementation. Second, rapid qualitative interviews were conducted with 15 guideline stakeholders, including 4 nurses, 3 physicians, 4 organizational managers, and 4 guideline methodologists, to gain a deeper understanding of the attributes and characteristics of guideline implementability. Third, a two-round Delphi process was conducted to screen and refine the items\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In total, 90 stakeholders from Asia, Africa, Europe, and North America participated, including 38 healthcare professionals, 29 healthcare organization managers, 15 implementation science experts, and 8 guideline methodologists. Fourth, potential items were reviewed by the investigative team and program managers for face validity and content validity. Twenty-six items were developed that represented five potential content dimensions of guidelines implementability: clarity, applicability, reliability, readability, and transparency. Fifth, a pilot survey using the tool to rate 30 CPGs was conducted to estimate the item discrimination, dispersion, correlation coefficients, and internal consistency\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Items with low relevance or poor consistency were removed to generate a pragmatic and optimized version of the tool\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The purpose was to refine items and improve the tool structure based on feedback from experts and guideline users. During the pilot survey, we removed the items with a resolution coefficient\u0026thinsp;\u0026lt;\u0026thinsp;0.50, a correlation coefficient\u0026thinsp;\u0026lt;\u0026thinsp;0.40, and a CITC value\u0026thinsp;\u0026lt;\u0026thinsp;0.20. Finally, the first version of the CPG-IAT with five dimensions and 21 items, were developed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGuidelines and Participants\u003c/h3\u003e\n\u003cp\u003eThe Scientific, Transparent, and Applicable Rankings (STAR) tool\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e has applied to evaluate Chinese CPGs and consensus published in 2020, 2021, and 2022\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. CPGs included in the most recent STAR assessment cycle served as the sampling frame for this study\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. As of October 24, 2024, the STAR Working Group had completed evaluations of guidelines published in 2022. To identify eligible guidelines and consensus, the STAR methodological rating group conducted systematic searches on August 24, 2022, and January 15, 2023. Chinese-language searches were performed in the China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, the Chinese Biomedical Literature Database (CBM), and the Chinese Medical Journals Database, while English-language searches were conducted in PubMed and Web of Science. The corresponding search periods were January 1 to July 31, 2022, and January 1 to December 31, 2022. In addition, supplementary searches were carried out in Hong Kong Journals Online, the Hong Kong and Macao Journals Network, the Macao Journal Paper Index, and the Taiwan Journal Paper Index\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e The evaluation working group consisted of a Steering Committee, a Secretariat, an Evaluation Panel, and a Quality Control Group. The Steering Committee comprised two members and was responsible for developing the evaluation framework and designing specific assessment procedures, overseeing the evaluation process, and providing methodological consultation and guidance. The Secretariat included two members and was responsible for supporting the Steering Committee in the organization and management of the evaluation activities, as well as for collecting, organizing, and archiving evaluation materials. The Evaluation Panel was composed of 60 individuals with clinical, guideline development, and/or prior rating experience, responsible for assessing the included guidelines. The Quality Control Group consisted of three authors and was responsible for reviewing and approving the evaluation results.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e The study was approved by the appropriate Institutional Review Boards prior to participants recruitment and informed consent was obtained prior to administering surveys.\u003c/p\u003e \u003cp\u003eMembers of the specialty committees within the working group completed an online training course and reviewed training materials on the CPG-IAT. Following the training, participating members underwent an assessment and certification process; only those who passed the certification were eligible to participate in the evaluation.\u003c/p\u003e \u003cp\u003e Members of the Evaluation Panel independently assessed each included guideline using the CPG-IAT. Based on the full text of the guideline and supplementary materials, evaluators judged whether each item\u0026rsquo;s criteria were met, selected an item score on a 5-point Likert scale (ranging from 1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree), and documented the rationale for each rating in the evidence form.\u003c/p\u003e \u003cp\u003eUpon completion of all evaluations, members of the Quality Control Group, with assistance from the Secretariat, reviewed the submitted materials to check the consistency between scores and supporting rationales, as well as to identify any internal inconsistencies or contradictions. If issues were identified, the materials were returned to the evaluators for revision. After independent evaluations were completed, the Secretariat cross-checked the ratings from two evaluators and calculated the intraclass correlation coefficient (ICC) to assess inter-rater agreement. When the ICC was \u0026le;\u0026thinsp;0.75, the guideline was assigned to a third evaluator by the Secretariat, and the ICC was recalculated based on the three sets of ratings. When the ICC exceeded 0.75, the final score for the guideline was calculated as the mean of the three evaluation results\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eData were randomized to be utilized for either the EFA (exploratory factor analysis) (n\u0026thinsp;=\u0026thinsp;131) or CFA (confirmatory factor analysis) (n\u0026thinsp;=\u0026thinsp;130). Exploratory factor analysis was used to derive and evaluate the factor structure of the tool using IBM SPSS. Principal axis factoring was selected for factor extraction because it allows for consideration of both systematic and random error and Promax oblique rotation was utilized for factor rotation as we assumed that derived factors would be correlated\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Item inclusion or exclusion was based on an iterative process in which items with relatively low primary loadings (e.g., \u0026lt;\u0026thinsp;0.40) or high cross-loadings (e.g., \u0026gt;\u0026thinsp;0.30) were removed\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The number of factors to be retained was determined based on parallel analysis, factor loadings, and interpretability of the factor structure as indicated in the rotated solution. Parallel analysis is among the better methods for determining the number of factors based on simulation studies\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Parallel analysis was based on estimation of 1000 random data matrices with values that correspond to the 95th percentile of the distribution of random data eigenvalues\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The random values were then compared with derived eigenvalues to determine the number of factors. Confirmatory factor analysis was performed to validate the factor structure of the CPG-IAT developed in exploratory factor analysis using AMOS 26.0 (IBM Corp., Armonk, NY, USA). Model fit was assessed using several empirically supported indices: the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). CFI and TLI values greater than 0.90, RMSEA values less than 0.10, and SRMR values less than 0.08 indicate acceptable model fit\u003csup\u003e[\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. If necessary, bootstrap Bollen-Stine \u003cem\u003eP\u003c/em\u003e would be utilized to assist confirmatory factor analysis when the model fit was not ideal\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe average variance extracted (AVE) was calculated using the output of the confirmatory factor analysis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Internal consistency, construct reliability, Spearman-Brown split-half reliability and test-retest reliability were analysed for the total sample during the formal investigation stage\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Internal consistency was evaluated using Cronbach\u0026rsquo;s alpha along with McDonald\u0026rsquo;s omega, with values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 considered acceptable\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The test-retest reliability was calculated by randomly selecting 50 guidelines from the total sample, and having different evaluators conduct the evaluation two weeks later. The inter-rater reliability was calculated by randomly selecting 2 evaluators to assess 10 guidelines.\u003c/p\u003e \u003cp\u003eThe psychometric and pragmatic evidence rating scale (PAPERS) was used to evaluate pragmatic of the CPG-IAT, and the evaluation content included normativity, cost of use, language, training cost, and length\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eThe information of guidelines\u003c/h2\u003e\n \u003cp\u003eA total of 291 guidelines were included in both the pilot survey and the formal investigation stage. Regarding specialty distribution, the included guidelines covered 30 medical specialties. Oncology had the highest number of included guidelines (n\u0026thinsp;=\u0026thinsp;36), while pathology, reproductive medicine, and cardiothoracic surgery had the fewest, with one guideline each (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExploratory factor analysis\u003c/h3\u003e\n\u003cp\u003eAn iterative approach was taken to conducting the factor analyses and item reduction. In the first iteration and consistent with our hypotheses, five factors were specified and all 21 items were included. The EFA results indicated that only one item met the factor loading criteria for the proposed fifth factor (loading\u0026thinsp;\u0026gt;\u0026thinsp;0.40 on the primary factor and \u0026lt;\u0026thinsp;0.30 loading on any secondary factors)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Factors defined by a single item are considered poorly defined and psychometrically unstable\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e; therefore, the fifth factor was not retained. The parallel analysis also suggested a four factor solution. Thus, we conducted the next EFA specifying four factors. The results suggested the removal of 5 items because of low primary factor loadings and/or high cross loadings. Thus, 16 items and 4 factors were retained in the final EFA.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eclinical practice guidelines implementability assessment tool and item statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTool, items, and total\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003esd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eev\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ev\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEFA factor loadings\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Methodological Rigor and Transparency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG01. Clearly identifies the intended users and target population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.702\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG02. Clearly describes the criteria for evidence selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.879\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG03. Evidence has been systematically searched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.916\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG04. Mentions plans or processes for guideline updating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.604\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG05. Provides a detailed description of the process used to develop recommendations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG06. Discloses key steps in the guideline development process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.807\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Recommendation Clarity and Interpretability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG07. Recommendations in the guideline text are clearly identifiable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.722\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG08. There is a clear linkage between recommendations and supporting evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.622\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG09. Provides the quality of evidence and strength of recommendation for each item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.484\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG10. The wording of recommendations is clear and concise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.767\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG11. The content is complete and logically organized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.677\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG12. The layout is well-designed and facilitates quick reference and use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.870\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Clinical Relevance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG13. Clearly defines the specific clinical questions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.807\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG14. Recommendations adequately address the clinical questions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.888\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. Actionability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG15. Recommendations provide practical steps for application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.778\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG16. The guideline specifies the implementation settings or applicable contexts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNote: N\u0026thinsp;=\u0026thinsp;261 for means and standard deviations; n\u0026thinsp;=\u0026thinsp;131 for other EFA derived statistics; sd= Standard deviation; ev= initial eigenvalue; v= variance accounted for before rotation; \u0026alpha;\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha; bold font for means and sd indicate the overall tool mean and sd; bold font for EFA factor loadings indicates the tool on which the items load.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays the factor means, item means, initial eigenvalues, variance accounted for by each factor, internal consistency reliabilities, and rotated factor loadings. Internal consistencies were high, ranging from 0.746 to 0.919. All items demonstrated acceptable corrected item-total correlations (CITC\u0026thinsp;\u0026ge;\u0026thinsp;0.30). Deletion of any individual item did not substantially improve Cronbach\u0026rsquo;s alpha, supporting the retention of all items for subsequent analyses. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, factor correlations ranged from 0.14 to 0.64, suggesting that the dimensions represent related but relatively distinct aspects of guideline implementability. Subscale labels were created based on an examination of the items and factor loadings presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Factor 1 was labeled \u0026lsquo;Methodological Rigor and Transparency\u0026rsquo; as it indicated the methodological foundations of guideline development, including transparent definition of target users and populations, explicit evidence selection criteria, and systematic processes for formulating and updating recommendations.Factor 2, \u0026lsquo;Recommendation Clarity and Interpretability\u0026rsquo; reflects how recommendations are structured, articulated, and presented to end users, encompassing clarity of recommendation identification, evidence linkage, language, and layout. Factor 3, \u0026lsquo;Clinical Relevance\u0026rsquo; represents the extent to which guidelines address clearly defined clinical questions and provide recommendations that directly respond to those questions. Factor 4, \u0026lsquo;Actionability\u0026rsquo; indicates whether guideline recommendations offer concrete implementation steps and clearly defined application contexts to support real-word use. The item explanations and examples of the final version of the CPG-IAT are presented in Supplementary 1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eclinical practice guidelines implementability assessment tool factor intercorrelations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Methodological Rigor and Transparency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Recommendation Clarity and Interpretability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Clinical relevance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Actionability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: N\u0026thinsp;=\u0026thinsp;131; All correlations, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eConfirmatory factor analysis\u003c/h3\u003e\n\u003cp\u003eConfirmatory factor analysis was used with a sample independent of the EFA sample to evaluate the factor structure identified in the EFA. The model was modified using the bootstrap Bollen\u0026ndash;Stine \u003cem\u003eP\u003c/em\u003e method\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The factor model demonstrated good fit as indicated by multiple fit indicators (n\u0026thinsp;=\u0026thinsp;130; CFI\u0026thinsp;=\u0026thinsp;0.99; TLI\u0026thinsp;=\u0026thinsp;0.99; RMSEA\u0026thinsp;=\u0026thinsp;0.03). The SRMR value was 0.11, indicating that the model did not fully reproduce the observed correlation structure. Nevertheless, given the satisfactory performance of incremental fit indices such as CFI, TLI and RMSEA, the overall model fit was considered acceptable. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the standardized factor loadings for the factor model, ranging from 0.373 to 0.904 and all factor loadings were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;0.001). McDonald\u0026rsquo;s omega coefficients indicated good to excellent internal consistency for all four factors, ranging from 0.74 to 0.92, with Cronbach\u0026rsquo;s alpha values similarly ranging from 0.746 to 0.920.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eConvergent validity\u003c/h2\u003e\n \u003cp\u003eConfirmatory factor analysis was performed to validate the factor structure obtained from exploratory factor analysis. Convergent validity, expressed as average variance extracted (AVE), ranged from 0.530 to 0.743 across dimensions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eReliability analysis\u003c/h2\u003e\n \u003cp\u003eConstruct reliability (CR) for each dimension ranged from 0.617 to 0.935, all above the minimum threshold of 0.6. The coefficient of the split-half reliability was 0.810, indicating good split-half reliability. For the 50 guidelines assessed, ICC values ranged from 0.825 to 0.999, all \u0026gt;\u0026thinsp;0.75, demonstrating good test-retest reliability. Two randomly selected raters independently evaluated 10 guidelines using the tool, yielding a Pearson correlation of 0.794 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), indicating significant correlation and high inter-rater agreement, confirming good inter-rater reliability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePragmatic evaluation\u003c/h2\u003e\n \u003cp\u003eThe tool demonstrated good normativity, showing concentration and distribution characteristics when applied to samples of \u0026ge;\u0026thinsp;100 guidelines (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The tool will be freely available to all researchers. It is comprehensible to individuals with at least a bachelor\u0026rsquo;s degree. Training is required but minimal, indicating acceptable assessor burden. The tool produces quantifiable scores with clear cut-offs, handles missing data via assessor follow-up, and can automatically calculate scores, reflecting minimal workload for evaluators. Finally, the tool contains 16 items, indicating an appropriate length.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e The current study describes the development of the measure of guideline implementability for guideline implementation, the CPG-IAT. We used an iterative process to develop items representing guideline implementability and then used quantitative data reduction techniques to develop a brief measure that may be easily and efficiently used for research and applied purposes. The CPG-IAT has the potential to optimize guidelines from within, by providing criteria and evaluation tools for assessing implementability before, during and after the guideline development process.\u003c/p\u003e \u003cp\u003e Although we originally proposed five factors of guideline implementability, quantitative analyses supported a four-factor model. Items related to evidence handling, methodological processes, and transparency clustered into a single Methodological Rigor and Transparency factor, while items concerning readability and usability converged into Recommendation Clarity and Interpretability. In contrast, items addressing the definition of clinical questions and the extent to which recommendations address those questions formed a distinct Clinical Relevance factor, and items focusing on implementation steps and applicable contexts were retained as Actionability. This four-factor solution demonstrated acceptable model fit and internal consistency in CFA, offering a more parsimonious and empirically supported representation of guideline implementability while preserving its key conceptual components. Transparency did not emerge as an independent factor because items related to transparency were empirically inseparable from methodological rigor, reflecting that, in practice, transparent reporting of development processes is closely intertwined with perceptions of evidence quality and rigor rather than representing a distinct construct.\u003c/p\u003e \u003cp\u003eThe CPG-IAT demonstrated strong internal consistency reliability, convergent validity, construct reliability, split-half reliability, test-retest reliability, inter-rater reliability and pragmatic. Given that the CPG-IAT is brief (i.e., 16 items), administration and use in guideline implementability evaluation can be very efficient with little respondent burden. The practicality of this brief scale is consistent with calls for measures that can be utilized in real-world settings where the efficiency of the research process is paramount\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe CPG-IAT shares a common objective with existing implementability appraisal instruments such as GLIA\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, GUIDE-IT\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, GLAFI (Guideline Language and Format Instrument )\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, and tool developed by Jin et al.\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, all of which aim to identify factors that influence the uptake of CPGs in clinical practice. However, the CPG-IAT advances the field in several important ways. First, different with GLIA, which is primarily grounded in Shiffman\u0026rsquo;s implementability framework and focuses on decidability and executability at the recommendation level\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, the CPG-IAT is built upon a more comprehensive theoretical foundation that integrates the CFGI framework, and extensive literature review, and multi-round expert consensus\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Second, different with many previous instruments that have not undergone comprehensive psychometric validation, the CPG-IAT has been systematically tested for reliability, validity, and pragmatic utility across diverse guideline types. In addition, compared with A-GIST\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which evaluates the actual outcomes of guideline implementation based on the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, the CPG-IAT focuses on assessing the intrinsic implementability of guidelines before, during, and after development. Together, these two tools form a complementary methodological landscape: the CPG-IAT addresses \u0026ldquo;how to make guidelines easier to implement,\u0026rdquo; whereas A-GIST examines \u0026ldquo;how successfully guidelines have been implemented.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThere are several strengths of CPG-IAT. First, the tool is grounded in a comprehensive theoretical framework that integrates conceptual, empirical, and expert-based evidence, ensuring its content is both systematic and aligned with established implementation science principles. Second, the tool has undergone rigorous psychometric testing-including validity, reliability, and pragmatic assessments-ensuring it is both methodologically sound and applicable across diverse types of CPGs. Third, its design supports use throughout the guideline development lifecycle, enabling developers, reviewers, and implementers to identify modifiable factors and target improvements at multiple stages. Together, these strengths position the CPG-IAT as an evidence-based, generalizable, and user-friendly instrument that can effectively guide the development of high-quality, implementable CPGs.\u003c/p\u003e \u003cp\u003eDespite the strengths, there are still some limitations. First, although the tool was developed through a rigorous process-including literature review, expert consensus, and psychometric testing-the evaluation relied primarily on CPGs published in 2022 from a single guideline repository. This may limit the generalizability of the findings to guidelines produced in different years, or development systems. Second, the assessment focuses on intrinsic features of guideline documents and development processes rather than contextual or system-level determinants of implementation. As a result, while the tool effectively evaluates document-level implementability, it does not replace organizational- or system-level readiness assessments. Third, although reliability, validity, and pragmatic utility were systematically examined, further testing in real-world implementation projects and across international guideline settings is needed to confirm cross-cultural applicability and predictive validity. Finally, as with most rating tools, the CPG-IAT involves a degree of subjectivity, and inter-rater consistency may vary depending on assessors\u0026rsquo; experience and familiarity with guideline methodology.\u003c/p\u003e \u003cp\u003e In summary, the CPG-IAT provides a systematically developed and psychometrically validated instrument for assessing the implementability of CPGs at the guideline document level. Although further cross-context validation is needed, the CPG-IAT represents a robust foundation for strengthening guideline quality and supporting future efforts to enhance the translation of evidence into practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA validated CPG-IAT, grounded in the COSMIN framework and informed by the CFGI, has been developed to appraise the intrinsic implementability of CPGs. CPG-IAT provides a structured and balanced assessment of guideline Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, clinical relevance, and actionability, offering a document-level evaluation that precedes real-world implementation. With its solid theoretical foundation, rigorous psychometric validation, and broad applicability across different types of CPGs, the CPG-IAT equips guideline developers, researchers, implementers, and policymakers with a practical tool to identify potential barriers embedded within guideline documents, enhance guideline quality before dissemination, and support the design of strategies to improve future uptake in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) of Southern Medical University (#Southern Medical Audit (2024) No. 012). All the participants signed the informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is funded through the following competitive grant: The Swiss Agency for Development and Cooperation (#81067392).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZDM conceptualized the paper, assisted with data analysis, developed the study methods, and drafted, reviewed, and edited the manuscript. GAA developed the study methods, reviewed and edited the manuscript. CYL reviewed and edited the manuscript. YN reviewed and edited the manuscript. HWJ reviewed and assisted with data analysis. WR reviewed and edited the manuscript. TJW reviewed and edited the manuscript. WYM assisted data collection. HZZ reviewed the manuscript. XD conceptualized the overall study, obtained funding, and edited and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all members in Acacia Lab for Implementation Science for their cooperation in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKastner M, Estey E, Bhattacharyya O. Better guidelines for better care: enhancing the implementability of clinical practice guidelines[J]. 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Am J Public Health, 1999,89(9):1322-1327.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"implementation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imps","sideBox":"Learn more about [Implementation Science](http://implementationscience.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/IMPS/default.aspx","title":"Implementation Science","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical practice guideline, Implementability, Assessment, Tool","lastPublishedDoi":"10.21203/rs.3.rs-8870282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8870282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eWe define guideline implementability as the characteristics of the guideline that reflect the extent to which it is likely to be adopted in clinical practice. Improving the intrinsic quality (e.g., context, format, language etc.) of clinical practice guidelines (CPGs) may be a cost-effective and broadly applicable approach. This study was aimed to develop the clinical practice guidelines implementability assessment tool (CPG-IAT) and test its psychometric properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study used the 2022 CPGs recorded in the STAR guideline repository as the evaluation sample. The evaluation team consisted of 60 members with clinical, guideline development, or prior rating experience, responsible for assessing the included guidelines. Guideline evaluation data were randomly assigned to be utilized for an exploratory factor analysis (n=131) or for a confirmatory factor analysis (n=130). Reliability and validity analyses were then conducted with the full sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThe exploratory factor analysis resulted in a 16-item tool with four dimensions representing Methodological Rigor and Transparency, Recommendation Clarity and Interpretability, clinical relevance and actionability. Confirmatory factor analysis supported a priori factor structure. The tool demonstrated excellent internal consistency reliability, convergent validity, construct reliability, split-half reliability, test-retest reliability, inter-rater reliability and pragmatic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThe CPG-IAT serves as a good tool for standardizing guideline development prior to their creation and for evaluating guideline implementability after development. The tools developed in this study not only provide a scientific basis for assessing the implementability of CPGs but also offer robust support for future research and practice in related fields.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration: \u003c/strong\u003eChina Clinical Trails Registry (ChiCTR2400086931); registered July 15, 2024. https://www.chictr.org.cn/\u003c/p\u003e","manuscriptTitle":"Development and validation of a clinical practice guidelines implementability assessment tool (CPG-IAT) based on the COSMIN framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 09:11:54","doi":"10.21203/rs.3.rs-8870282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T17:11:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T19:48:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176725798952184195642808405744646342853","date":"2026-03-25T12:40:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T20:07:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T19:14:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T05:39:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Implementation Science","date":"2026-02-13T09:36:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"implementation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imps","sideBox":"Learn more about [Implementation Science](http://implementationscience.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/IMPS/default.aspx","title":"Implementation Science","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d5d3537-9157-42f6-8603-f41fbddfd6ba","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-29T17:11:30+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T12:10:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 09:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8870282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8870282","identity":"rs-8870282","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}