Implementability of Clinical Practice Guidelines: the Review and Development of a Comprehensive Framework for Guideline Implementability (CFGI)

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Our objectives were to create a comprehensive and evidence-informed framework of guideline implementability (CFGI). Methods: A mixed-methods approach was used. Based on a systematic literature review of six databases as the foundation, the initial version of the CFGI was created, followed by external consultations to gather feedback and natural language processing tool-assisted classificationto refine the framework. To get external validation of the CFGI from expert feedback at an international conference Results: 15 studies related to guideline implementability were identified from the systematic literature review. The first version of CFGI was compiled, including 6 domains. Feedback on the first version was received from 16 stakeholders, including clinicians, nurses, medical managers, and guideline methodologists, combined with natural language processing tool-assisted classification. The final version of the CFGI is comprised of 6 core domains, containing 21 constructs: (1) Scope and purpose; (2) Clarity and consistency of recommendations; (3) Development and evidence base; (4) Structure and Contents; (5) Development team and transparency; and (6) Implementation environment and tools. Twenty-nine experts participated in the external validation, and the results showed that CFGI had good rationality, importance, clarity, feasibility, and necessity. Conclusions : The development of the CFGI provides a systematic theoretical basis for the development and implementation of future CPGs, which will help to enhance the implementability of guidelines and facilitate their promotion and application in different medical settings. Future research can further validate and apply the CFGI, explore its effectiveness and feasibility in actual operation. Preventive Medicine Clinical practice guideline implementability framework CFGI Figures Figure 1 Figure 2 Figure 3 1. Introduction Clinical practice guidelines (CPGs) are recommendations based on the best available evidence, balancing the benefits and harms of different interventions, to provide the optimal health care for patients [ 1 ] . Over 250,000 CPGs have been published worldwide [ 2 ] . Despite this, their utilization remains suboptimal and various strategies to improve their use have not been optimised [ 3 ] , resulting in considerable waste of time and resources [ 4 ] . Barriers to guideline implementation can be categorized into intrinsic factors—related to the guidelines themselves (e.g., content, format, language)—and extrinsic factors—contextual barriers (e.g., medical personnel, medical institutions, local policies) [ 5 , 6 ] . Addressing contextual barriers is crucial but often beyond the control of guideline developers and users. In contrast, intrinsic barriers can be mitigated by developers during the guideline creation process to enhance implementability. Therefore, understanding and improving the implementability of guidelines is essential for advancing healthcare across diverse health issues and contexts. “Implementability” of CPGs refers to “a set of guideline characteristics that predict how effectively that CPG can be implemented” [ 4 ] . Several theoretical frameworks, models and scales have been developed to explain and measure the implementability of clinical guidelines, such as the Guideline Implementability Appraisal (GLIA) [ 4 ] , Guideline Implementability Tool (GUIDE-IT) [ 7 ] , Guideline Implementability Decision Excellence Model (GUIDE-M) [ 8 ] . However, the varied focuses of these tools limit their systematic application in complex clinical environments. For example, GLAFI [ 6 ] focuses solely on the language and format of CPGs, while the checklist developed by Bai Xue et al. [ 9 ] is more applicable to Traditional Chinese Medicine CPGs. Addtionally, many of these tools, such as GLIA (2005) [ 4 ] and AGREE (Appraisal of Guidelines, REsearch and Evaluation) (2003) [ 10 ] , were developed more than a decade ago [ 4 , 10 ] , and may warrant updates to the current medical contexts. Furthermore, many of those tools were developed solely based on literature reviews [ 3 , 6 ] . In this study, we developed a comprehensive and integrated theoretical framework for clinical practice guideline implementability through a three-phase process. In Phase 1, we synthesized the existing theoretical frameworks, models, and scales. Phase 2 involved consultation meetings with stakeholders, during which we created the initial version of the framework. In Phase 3, we undertook iterative revisions, leading to a refined final version known as the Comprehensive Framework for Guideline Implementability (CFGI). 2. Methods This study comprises three interrelated and progressive stages (Figure 1). Ethics approval was obtained from the Institutional Review Board (IRB) of Southern Medical University in Guangzhou, China (# Southern Medical Audit (2024) No. 012). The study was prospectively registered at the China Clinical Trails Registry (ChiCTR2400086931) on July 15, 2024 and funded by the Swiss Agency for Development and Cooperation (# 81067392) and the National Natural Science Foundation of China (NSFC) (# 72304007). 2.1 A comprehensive systematic review of the prevailing concepts, theories, models, frameworks, and scales Initially, a systematic review and meta-aggregation approach [11] was employed to develop the first version of the CFGI by integrating existing concepts, theories, models, frameworks, and scales related to the clinical practice guideline implementability. Search strategy The reporting of this systematic review adhered to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement [12] .The selected databases included PubMed, Web of Science, Google Scholar, China National Knowledge Infrastructure (CNKI), Wanfang Database and Weip databases. Two systematic reviews were conducted: the first covered records from the earliest available date to April 21, 2023, and the second focused on articles published between January 1, 2023, and August 18, 2024. The search terms used were “guide OR guides OR guideline OR guidelines” and “implement OR implementability OR barrier OR barriers OR facilitator OR facilitators”. A detailed description of our comprehensive search strategy is provided in Supplement 1. Inclusion criteria were: - Concepts, theories, models, frameworks, and scales related to CPG implementability. - Articles written in English or Chinese. Exclusion criteria were: - Literature with an unclear peer review process (grey literature): including tutorials, toolkits, editorials, extended abstracts or research summaries. - Literature where the full text could not be obtained. Data Extraction Data extracted from each study included publication year and first author, location, name and type of the concepts/ theories/ models/ frameworks or scales, development methods, reliability and validity verification methods, and the main content of the concepts/ theories/ models/ frameworks or scales. The first author extracted and tabulated the relevant data from the studies, and the second author double-checked the information. Research findings related to the implementability of CPGs, derived from the included concepts, theories, models, frameworks, or scales, were synthesized using a meta-aggregation approach. Two researchers (ZDM and WYM) categorised the findings based on thematic similarities. These categories were then aggregated to produce a cohesive set of synthesized findings. Based on the synthesized results, the research team engaged in discussions to summarize the first version of CFGI. 2.2 External consultation Participants We recruited participants with experience in guideline implementation, including clinicians, nurses, medical managers, and guideline methodologists, Our objective was to be as inclusive as possible , allowing for diverse perspectives; therefore, we did not specify a target sample size. While many qualitative studies determine sample size based on saturation [13] , we opted to prioritize inclusivity beyond code saturation [14] . Process We used rapid qualitative analysis to explore the stakeholders' perspectives of the domains and constructs of the CFGI. Participants who agreed to participate received an e-mail with a meeting invitation and the content of the first version of the CFGI. This process ran from November to December of 2023 and January to February of 2024. During the formal meetings, two authors (ZDM and WYM) presented the project objectives and each domain of the CFGI for discussion, followed by soliciting participant feedback. Interview structure The interview team comprised an interviewer and two note-takers. The interviewer followed a structured guide, while the note-takers recorded notes into a predefined and template. Note-takers were allowed to ask questions during the interview to clarify a concept or statement. Analysis Plan We used rapid qualitative analysis with targeted data reduction [15] . While interviews were recorded, they were not transcribed. Two investigators (ZDM and WYM) met and reviewed all notes taken by the interviewer and note-takers during interviews with stakeholders. Informed by domains and constructs in the CFGI and a-priori areas, the investigators used thematic analysis and the matrix method to identify the main responses across all interviews and debrief notes [16-18] . Rigor and validity were established by independently coding and assigning these data in the matrix and discussions with the larger team. The research team's positionality was influenced by the belief that increased guideline usage would positively impact healthcare and that identifying barriers to implementation is essential. Disagreements in coding were resolved through discussion among the coders and input from the larger team. 2.3 T he final version of CFGI This study further classified and adjusted the composition of the CFGI with the following steps: Preliminary classification of constructs: Based on stakeholder feedback, we examined classification issues related to certain constructs. All constructs were input into ChatGPT 4.0, which classified them and assigned titles to each category, providing a foundation for the final framework. Natural language processing tool-assisted classification: Following ChatGPT's classification suggestions, we further refined the categorization based on stakeholders' practical operational requirements, the clarity of the internal logic of the theoretical framework, and triangulation with existing classification criteria in the literature. Team discussion and revision: Finally, the research team reviewed the preliminary results and made modifications to the classifications and naming of constructs based on their respective domain experience and understanding of the framework, ensuring that the classifications of the final version were scientific, reasonable and actionable, that could effectively support the implementability evaluation of the guidelines. Confirmation of the final framework of CFGI: After two rounds of discussions and revisions by the team members, the classification structure of constructs was confirmed and the final version of the CFGI accepted. The framework is intended to serve as the basis for future research by providing a theoretical basis for the evaluation of CPG implementability. External validation of the CFGI: We introduced CFGI and distributed surveys to the experts attending the 2024 Global Implementation Society (GIS) & Nigeria Implementation Science Alliance (NISA) Conference in Abujia, Nigeria. We collected experts’ feedback on the rationality, importance, clarity, feasibility, and necessity of each domain of the CFGI, using a scoring system from 1 to 5, where 1 indicated the lowest degree and 5 the highest. The mean ± standard deviation (SD) and full score ratio of each evaluation index in each domain were calculated to show the evaluation results. The coefficient of variation (CV) was calculated to assess expert consensus on the domains; a lower CV indicates higher agreement among experts, with values generally accepted to be <0.25. No limit was imposed on the sample size. 3. Results As shown in the PRISMA flow diagram (Figure 2), the initial electronic database search identified 8,268 articles, of which 849 were duplicates and subsequently removed. We excluded 6,441 articles after screening the titles and abstracts against the eligibility criteria, leaving 978 articles for further examination. However, 35 of these studies lacked full text and were excluded. Following a thorough review of the remaining 943 full-text studies, 928 were excluded, resulting in 15 papers that met the inclusion criteria. 3.1 Characteristics of studies Table 1 summarizes the characteristics of the 15 studies reviewed [3, 4, 6-10, 19-26] , which were published between 2003. These studies were conducted in various countries: Canada (n=7), China (n=3), the USA (n=2), and Ireland (n=1), encompassing data from a total of 11 countries, with one study including data from 46 countries. Among the 15 studies, one specifically addressed guidelines for Traditional Chinese Medicine (TCM) [9] , while the others focused on general CPGs. The studies aimed at different objectives: some developed concepts [19-21, 25] , others established models [21] , formulated frameworks [3, 6, 7] , and created measurement scales [4, 9, 10, 22-24, 26] . The development methods employed varied. Some studies relied solely on literature reviews [3, 6, 19-21, 25] , while others used expert opinion and consensus [4, 24] , qualitative research [7] , and a combination of literature review and expert consensus [9, 10, 21-23, 26] . Among the seven studies that developed scales, one either did not perform any assessments of reliability or validity [9] . Of those that did, some addressed both aspects [10, 22-24] , while others only evaluated one [4, 26] . 3.2 Findings of the review In the included studies, 74 constructs were identified and classified (Supplement 2). These constructs were aggregated into 16 synthesised dimensions based on intrinsic meanings and overlapping content. After two rounds of discussions within the team, the first version of the CFGI was established. The content of the first version of CFGI included six domains and 38 constructs, namely clarity and consistency of recommendations (8 constructs), detail and usability (7 constructs), contextual relevance (4 constructs), implementation support (10 constructs), transparency/ authority and conflict of interest (6 constructs), feedback and updating procedures (3 constructs). The details of the findings from the literature for each domain in the first version of CFGI can be found in Supplement 3. 3.3 External consultation Semi-structured focus group interviews were conducted via a video conferencing platform, involving a total of 16 experts from China (Table 2). Participants included clinicians, nurses, medical managers, and guideline methodologists from various regions across the country. Table 2 Demographic characteristics of the interviewees (n=16) Characteristic n (%)/ mean ±SD Characteristic n (%) Gender Occupation Male 5 (31.3%) Clinican 3 (18.8%) Female 11 (68.8%) Nurse 4 (25.0%) Age (years) 27~43 32.4±4.9 Medical manager 4 (25.0%) Workplace Guideline methodologist 5 (31.3%) East region 7 (43.8%) Professional title Middle 5 (31.3%) Junior title 9 (56.3%) West 4 (25.0%) Intermediate title 4 (25.0%) Educational level Advanced title 3 (18.7%) Bachelor’s degree 5 (31.3%) Ethnic Group Master’s or Doctoral degree 11 (68.8%) Ethnic Han 15 (93.8%) Working Experience (years) 3~23 10.4±5.7 Other 1 (6.2%) Overall, respondents expressed strong support for the synthesized domains and constructs and provided revisions. All participants unanimously agreed that each domain, except for "transparency, authority, and conflict of interest," significantly influences guideline implementability. For this domain, clinicians, in particular, indicated that they were less concerned about the funding sources and production processes of the guidelines, focusing instead on the origins of the recommendations. Respondents discussed each construct, leading to a reduction in the number of constructs from 38 to 21. Thirteen constructs were removed due to vagueness, five were deemed unnecessary, and six were merged due to their similarities (see Supplement 4). Additionally, six new constructs were introduced. The criteria for removal emphasized flexibility, practicality, and traceability. Constructs that were too narrow, ambiguous, or overly similar were excluded. Detailed reasons for the inclusion or exclusion of each construct in the first version of the CFGI are provided in Supplement 4. For instance, the construct “The recommendations align with implementation objectives” was removed because “implementation objectives” can vary across contexts. Without specifying the objectives, this construct was too vague, and the term “align” lacked clarity regarding how or to what extent the recommendations were consistent with those objectives. In another example, the constructs “Recommendations are clear and unambiguous” and “The language of the guidelines is clear, simple, and concise” were merged, as they conveyed similar meanings. 3. 4 Final version of the CFGI Based on the external consultation, the final version of the CFGI comprised 21 constructs divided into six domains, refined through the natural language processing assistance and team discussion. Table 3 crosswalks the CFGI constructs related to implementability to 15 other frameworks. The final CFGI domains (Figure 3) and constructs are: Scope and purpose (5 constructs) : Guidelines should clearly describe the main content, scope of application, and target groups so that users can effectively apply the recommendations; Clarity and consistency of recommendations (4 constructs) : Emphasis is placed on clarity, applicability, implementation details, and support to ensure the effective implementation and adaptability of recommendations; Development and evidence base (5 constructs) : It highlights the formation process of recommendations, selection criteria and quality of evidence to ensure ensure scientific reliability; Structure and content (2 constructs) : The rationality of the guidelines' layout and term definitions is crucial for user comprehension and application; Development team and transparency (3 constructs) : Emphasizing stakeholder engagement, disclosure of potential conflicts of interest, and team integrity ensures the guidelines' impartiality and reliability; Implementation environment and tools (2 constructs): guidelines should provide identifiable recommendations and a format that facilitates implementation. A total of 29 experts completed the survey for external validation of CFGI (Table 4). The mean ± SD ratings for rationality, importance, clarity, feasibility, and necessity across the six dimensions of the CFGI exceeded 4.0. The coefficient of variation (CV) was less than 0.25, indicating consistent evaluations among experts (Table 5). Table 4 Demographic characteristics of the experts (n=29) Characteristic n (%) Characteristic n (%) Gender Years of Working Male 22 (75.9%) 5-10 9 (31.0%) Female 7 (24.1%) 11-20 14 (48.3%) Age (year) 21~ 6 (20.7%) 20-29 1 (3.4%) Working field 30-39 12 (41.4%) Health services 9 (31.0%) 40-49 10 (34.5%) Clinical medicine 5 (17.2%) 50~ 6 (20.7%) Health statistics 5 (17.2%) Geographic location Guideline methodology 5 (17.2%) Africa 21 (72.4%) Clinical pharmacy 2 (6.9%) Europe 4 (13.87%) Clinical nursing 1 (3.4%) Asia 3 (10.3%) Health policy 1 (3.4%) North America 1 (3.4%) Hospital management 1 (3.4%) Educational level Bachelor’s degree 1 (3.4%) Master’s degree 17 (58.6%) Doctoral degree 11 (37.9%) 4. Discussion Numerous clinical practice guidelines have been developed at considerable cost and effort [ 27 ] , however, many remain unused or poorly implemented [ 28 , 29 ] . This paper presents the Comprehensive Framework for Guideline Implementability (CFGI) that consists of 21 constructs organized into six domains, developed through a systematic synthesis of reviews and expert consensus. We reflect on the CFGI development process and key decisions made. Starting with 16 dimensions from a systematic literature review, we created the first version with 6 domains and 38 constructs. Through expert consensus, we refined this to 21 constructs while improving clarity and logic. Ambiguous or redundant constructs were removed to avoid duplication and enhance structural coherence, while practical constructs were added to address real-world needs, such as improving the operability of recommendations and supporting clinical applications. These changes made the framework more relevant and applicable. For example, constructs in the "clarity and consistency of recommendations" domain now specify implementation steps more clearly, while the "development process and evidence base" domain emphasizes the link between evidence selection and recommendations. The final CFGI version, with 6 domains and 21 constructs, presents a streamlined, intuitive framework that provides guidance for guideline implementability. As a comprehensive framework, the CFGI harnesses many benefits of existing frameworks while generating its unique features. First, the CFGI is a comprehensive framework that offers a broader and more systematic taxonomy related to guideline implementability. Table 3 crosswalks the CFGI constructs related to implementability to 15 other frameworks, suggesting that many other frameworks miss some key constructs. For instance, proper formatting, layout, and structure, clearly defined key terms and definitions, and recommendations being feasible in clinical settings with specific implementation steps involved are all essential for implementation but are often missing in most frameworks. Second, the CFGI offers a logical and intuitive structure of domains, moving from the "Scope and Purpose" of the overall guideline, to the "Clarity and Consistency of Recommendations," and then following with how those recommendations involve the "Development and Evidence Base," their "Structure and Content," and finally addressing the "Development Team and Transparency" behind them, culminating in the implementation environment and tools. A popular organization of domains in other frameworks follows “mediators” to implementability, such as “accessibility, communicability, and executability” in Jin Yinghui et al. [ 26 ] , and usability, adaptability, validity, applicability, etc., in Gagliardi et al. [ 3 ] . We chose to organize the domains by “content” that is empirically or theoretically linked to implementability. We believe organizing by content is more intuitive and specific, making assessment easier as well. Third, the CFGI is easier to apply and operationalize in assessments. Some constructs of the CFGI can be objectively assessed (presence or absence of the assessed constructs), such as “recommendations are supported by references” or “guideline presents the development team.” Others still require a subjective assessment but minimize that subjectivity by narrowing the assessment range. For example, “The guideline clearly describes the methods used to form the recommendations” allows relatively straightforward judgment on whether the method involved in developing the recommendations is “clear,” despite this being a somewhat subjective assessment. Finally, each constructs in the CFGI is empirically testable, laying the foundation for rigorous empirical testing of the validity of the CFGI to develop it into a scale in the future. We have planned an experiment based on a factorial trial wherein clinical practice guidelines will be adjusted to reflect the absence or presence or degree of the constructs involved in the CFGI, and then empirically testing whether those components actually lead to better implementability. The CFGI serves distinct purposes compared to general guideline quality assessment. While quality evaluation of guidelines often examines overall scientific validity [ 10 ] , the CFGI specifically focuses on assessing implementability, primarily at the individual recommendation level. It serves two main audiences: guideline developers and guideline users. For developers, the CFGI identifies fixable weaknesses during the guideline development process, allowing for modifications before finalization. For users, the framework assists in selecting more implementable guidelines and developing targeted implementation strategies to overcome identified obstacles. By providing insights into the factors affecting implementability, the CFGI empowers users to make informed decisions that enhance the likelihood of successful guideline adoption in practice. Strengths and limitations The CFGI follows a robust, multi-step development process. It builds on existing theoretical frameworks for the implementability of clinical practice guidelines and incorporates stakeholder input. A comprehensive review of evaluation tools extracted relevant implementability content. Expert consensus refined the framework, while natural language processing and team discussions helped classify domains. The final version was externally validated at an international conference, gathering feedback from 29 experts. This meticulous process improved the framework's scientific rigor and its global applicability. We should note two limitations. First, while the framework has laid the foundation for quantifiable assessment, it has not yet been developed into a scale. Proper weighting and scoring methods need to undergo more rigorous psychometric evaluations. Second, although our constructs are based on theoretical foundations from the literature, these constructs still need to be empirically validated in terms of their ability to predict guideline implementation. However, as previously mentioned, we have planned a factorial trial to address this issue. 5. Conclusion Through a systematic review of existing frameworks and models, along with expert consultation, we developed the Comprehensive Framework for Guideline Implementability (CFGI) to systematically address factors affecting guideline implementation. The CFGI provides developers and users of clinical practice guidelines with tools to enhance guideline implementability. This framework emphasizes multi-stakeholder involvement and establishes a foundation for future validation studies aimed at improving guideline implementation and healthcare outcomes. Declarations Ethics approval and consent to participate: Ethics approval was obtained from the Institutional Review Board (IRB) of Southern Medical University in Guangzhou, China (# Southern Medical Audit (2024) No. 012). All participants provided informed consent prior to each interview and each survey. Consent for publication: Not applicable Availability of data and materials: Data are available in a public. Competing interests: The authors declare that they have no competing interest. Dong (Roman) Xu, one of the authors of this paper, is an editor for Implementation Science Communications Journal. Funding: This study was funded by the Swiss Agency for Development and Cooperation (# 81067392) and the National Natural Science Foundation of China (NSFC) (# 72304007). Authors' contributions : XD conceived the project concept. XD, ZDM and Gregory reviewed and commented on the design and methods. ZDM developed the first draft, along with XD, Gregory and Alison. ZDM, WYM, LSS, YN, SZW, LZL and YLJ performed the literature screening, data extraction and data collection. XD, William, WYN, David, CK, CYL and ZPX reviewed the content and edited the manuscript. All coauthors participated in the revision and approved this manuscript. Acknowledgements: The authors thank all the interviewees and experts who contributed to this study by generously sharing their invaluable insights and experiences. References Trustworthy I O M U, Guidelines C P. Clinical Practice Guidelines We Can Trust[M]. Washington (DC): National Academies Press (US), 2011. De Hert S, Paula-Garcia W N. Implementation of guidelines in clinical practice; barriers and strategies[J]. Curr Opin Anaesthesiol, 2024,37(2):155-162. Gagliardi A R, Brouwers M C, Palda V A, et al. 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Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients[J]. Am J Public Health, 2022,112(6):913-922. Zhang L, Liang H, Luo H, et al. Quality in screening and measuring blood pressure in China's primary health care: a national cross-sectional study using unannounced standardized patients[J]. Lancet Reg Health West Pac, 2024,43:100973. Tables Table 1, 3 and 5 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Supplement1Retrievalstrategy.docx Supplement 1 Supplement2Tablesynthsizingoutcomes.docx Supplement 2 Supplement3ThedetailsofthefirstversionofCFGI.docx Supplement 3 Supplement4.docx Supplement 4 Table122025228.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6182899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":425913085,"identity":"3b72f5d2-de9d-49a5-bf98-432b5bb5b92d","order_by":0,"name":"Dongmei Zhong","email":"","orcid":"https://orcid.org/0000-0002-0886-6445","institution":"1.School of Public Health, Southern Medical University, Guangzhou, China \t2.Center for World Health Organization Studies, School of Health Management, Southern Medical University, Guangzhou, China \t3.Acacia Lab for Implementation Science, Dermatology Hospital of Southern Medical University (SMU) Institute for Global Health (SIGHT), Guangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Zhong","suffix":""},{"id":425913086,"identity":"91b9a0dd-a843-48b3-81bb-3a1526e64548","order_by":1,"name":"Yimin Wu","email":"","orcid":"","institution":"The Second Clinical School of Medicine, Southern Medical University, Guangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Wu","suffix":""},{"id":425913087,"identity":"b0458632-aab8-4520-8e44-4bd52f245927","order_by":2,"name":"Gregory A Aarons","email":"","orcid":"","institution":"Department of Psychiatry, University of California San Diego, La Jolla, CA, United States","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"A","lastName":"Aarons","suffix":""},{"id":425913088,"identity":"1c3fa64b-a627-4cf6-a7a9-f87d83e35d51","order_by":3,"name":"Alison M Hutchinson","email":"","orcid":"","institution":"6.School of Nursing 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11:26:53","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":63313,"visible":true,"origin":"","legend":"","description":"","filename":"Table122025228.docx","url":"https://assets-eu.researchsquare.com/files/rs-6182899/v1/588c1dfd9f9c63a6f746c258.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImplementability of Clinical Practice Guidelines: the Review and Development of a Comprehensive Framework for Guideline Implementability (CFGI)\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClinical practice guidelines (CPGs) are recommendations based on the best available evidence, balancing the benefits and harms of different interventions, to provide the optimal health care for patients\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Over 250,000 CPGs have been published worldwide\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite this, their utilization remains suboptimal and various strategies to improve their use have not been optimised\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, resulting in considerable waste of time and resources\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Barriers to guideline implementation can be categorized into intrinsic factors\u0026mdash;related to the guidelines themselves (e.g., content, format, language)\u0026mdash;and extrinsic factors\u0026mdash;contextual barriers (e.g., medical personnel, medical institutions, local policies)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Addressing contextual barriers is crucial but often beyond the control of guideline developers and users. In contrast, intrinsic barriers can be mitigated by developers during the guideline creation process to enhance implementability. Therefore, understanding and improving the implementability of guidelines is essential for advancing healthcare across diverse health issues and contexts.\u003c/p\u003e \u003cp\u003e\u0026ldquo;Implementability\u0026rdquo; of CPGs refers to \u0026ldquo;a set of guideline characteristics that predict how effectively that CPG can be implemented\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Several theoretical frameworks, models and scales have been developed to explain and measure the implementability of clinical guidelines, such as the Guideline Implementability Appraisal (GLIA)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, Guideline Implementability Tool (GUIDE-IT)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, Guideline Implementability Decision Excellence Model (GUIDE-M)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, the varied focuses of these tools limit their systematic application in complex clinical environments. For example, GLAFI\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e focuses solely on the language and format of CPGs, while the checklist developed by Bai Xue et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e is more applicable to Traditional Chinese Medicine CPGs. Addtionally, many of these tools, such as GLIA (2005)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and AGREE (Appraisal of Guidelines, REsearch and Evaluation) (2003)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, were developed more than a decade ago\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and may warrant updates to the current medical contexts. Furthermore, many of those tools were developed solely based on literature reviews\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e In this study, we developed a comprehensive and integrated theoretical framework for clinical practice guideline implementability through a three-phase process. In Phase 1, we synthesized the existing theoretical frameworks, models, and scales. Phase 2 involved consultation meetings with stakeholders, during which we created the initial version of the framework. In Phase 3, we undertook iterative revisions, leading to a refined final version known as the Comprehensive Framework for Guideline Implementability (CFGI).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study comprises three interrelated and progressive stages (Figure 1). Ethics approval was obtained from the Institutional Review Board (IRB) of Southern Medical University in Guangzhou, China (# Southern Medical Audit (2024) No. 012). The study was prospectively registered at the China Clinical Trails Registry (ChiCTR2400086931) on July 15, 2024 and funded by the Swiss Agency for Development and Cooperation (# 81067392) and the National Natural Science Foundation of China (NSFC) (# 72304007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1 A comprehensive systematic review of the prevailing concepts, theories, models, frameworks, and scales\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, a systematic review and meta-aggregation approach\u003csup\u003e[11]\u003c/sup\u003e was employed to develop the first version of the CFGI by integrating existing concepts, theories, models, frameworks, and scales related to the clinical practice guideline implementability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reporting of this systematic review adhered to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement\u003csup\u003e[12]\u003c/sup\u003e.The selected databases included PubMed, Web of Science, Google Scholar, China National Knowledge Infrastructure (CNKI), Wanfang Database and Weip databases. Two systematic reviews were conducted: the first covered records from the earliest available date to April 21, 2023, and the second focused on articles published between January 1, 2023, and August 18, 2024. The search terms used were \u0026ldquo;guide OR guides OR guideline OR guidelines\u0026rdquo; and \u0026ldquo;implement OR implementability OR barrier OR barriers OR facilitator OR facilitators\u0026rdquo;. A detailed description of our comprehensive search strategy is provided in Supplement 1.\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eInclusion criteria were:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e- Concepts, theories, models, frameworks, and scales related to CPG implementability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Articles written in English or Chinese.\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eExclusion criteria were:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e- Literature with an unclear peer review process (grey literature): including tutorials, toolkits, editorials, extended abstracts or research summaries.\u003c/p\u003e\n\u003cp\u003e- Literature where the full text could not be obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extracted from each study included publication year and first author, location, name and type of the concepts/ theories/ models/ frameworks or scales, development methods, reliability and validity verification methods, and the main content of the concepts/ theories/ models/ frameworks or scales. The first author extracted and tabulated the relevant data from the studies, and the second author double-checked the information.\u003c/p\u003e\n\u003cp\u003eResearch findings related to the implementability of CPGs, derived from the included concepts, theories, models, frameworks, or scales, were synthesized using a meta-aggregation approach. Two researchers (ZDM and WYM) categorised the findings based on thematic similarities. These categories were then aggregated to produce a cohesive set of synthesized findings. Based on the synthesized results, the research team engaged in discussions to summarize the first version of CFGI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 External consultation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited participants with experience in guideline implementation, including clinicians, nurses, medical managers, and guideline methodologists, Our objective was to be as inclusive as possible , allowing for diverse perspectives; therefore, we did not specify a target sample size. While many qualitative studies determine sample size based on saturation\u003csup\u003e[13]\u003c/sup\u003e, we opted to prioritize inclusivity beyond code saturation\u003csup\u003e[14]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcess\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used rapid qualitative analysis to explore the stakeholders\u0026apos; perspectives of the domains and constructs of the CFGI. Participants who agreed to participate received an e-mail with a meeting invitation and the content of the first version of the CFGI. This process ran from November to December of 2023 and January to February of 2024. During the formal meetings, two authors (ZDM and WYM) presented the project objectives and each domain of the CFGI for discussion, followed by soliciting participant feedback.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterview structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interview team comprised an interviewer and two note-takers. The interviewer followed a structured guide, while the note-takers recorded notes into a predefined and template. Note-takers were allowed to ask questions during the interview to clarify a concept or statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis Plan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used rapid qualitative analysis with targeted data reduction\u003csup\u003e[15]\u003c/sup\u003e. While interviews were recorded, they were not transcribed. Two investigators (ZDM and WYM) met and reviewed all notes taken by the interviewer and note-takers during interviews with stakeholders. Informed by domains and constructs in the CFGI and a-priori areas, the investigators used thematic analysis and the matrix method to identify the main responses across all interviews and debrief notes\u003csup\u003e[16-18]\u003c/sup\u003e. Rigor and validity were established by independently coding and assigning these data in the matrix and discussions with the larger team.\u0026nbsp;The research team\u0026apos;s positionality was influenced by the belief that increased guideline usage would positively impact healthcare and that identifying barriers to implementation is essential. Disagreements in coding were resolved through discussion among the coders and input from the larger team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eT\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ehe final version of CFGI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study further classified and adjusted the composition of the CFGI with the following steps:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePreliminary classification of constructs: Based on stakeholder feedback, we examined classification issues related to certain constructs. All constructs were input into ChatGPT 4.0, which classified them and assigned titles to each category, providing a foundation for the final framework.\u003c/li\u003e\n \u003cli\u003eNatural language processing tool-assisted classification: Following ChatGPT\u0026apos;s classification suggestions, we further refined the categorization based on stakeholders\u0026apos; practical operational requirements, the clarity of the internal logic of the theoretical framework, and triangulation with existing classification criteria in the literature.\u003c/li\u003e\n \u003cli\u003eTeam discussion and revision: Finally, the research team reviewed the preliminary results and made modifications to the classifications and naming of constructs based on their respective domain experience and understanding of the framework, ensuring that the classifications of the final version were scientific, reasonable and actionable, that could effectively support the implementability evaluation of the guidelines.\u003c/li\u003e\n \u003cli\u003eConfirmation of the final framework of CFGI: After two rounds of discussions and revisions by the team members, the classification structure of constructs was confirmed and the final version of the CFGI accepted. The framework is intended to serve as the basis for future research by providing a theoretical basis for the evaluation of CPG implementability.\u003c/li\u003e\n \u003cli\u003eExternal validation of the CFGI: We introduced CFGI and distributed surveys to the experts attending the 2024 Global Implementation Society (GIS) \u0026amp; Nigeria Implementation Science Alliance (NISA) Conference in Abujia, Nigeria. We collected experts\u0026rsquo; feedback on the rationality, importance, clarity, feasibility, and necessity of each domain of the CFGI, using a scoring system from 1 to 5, where 1 indicated the lowest degree and 5 the highest. The mean \u0026plusmn; standard deviation (SD) and full score ratio of each evaluation index in each domain were calculated to show the evaluation results. The coefficient of variation (CV) was calculated to assess expert consensus on the domains; a lower CV indicates higher agreement among experts, with values generally accepted to be \u0026lt;0.25. No limit was imposed on the sample size.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. Results","content":"\u003cp\u003eAs shown in the PRISMA flow diagram (Figure 2), the initial electronic database search identified 8,268 articles, of which 849 were duplicates and subsequently removed. We excluded 6,441 articles after screening the titles and abstracts against \u0026nbsp; the eligibility criteria, leaving 978 articles for further examination. However, 35 of these studies lacked full text and were excluded. Following a thorough review of the remaining 943 full-text studies, 928 were excluded, resulting in 15 papers that met the inclusion criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Characteristics of studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the characteristics of the 15 studies reviewed\u003csup\u003e[3, 4, 6-10, 19-26]\u003c/sup\u003e , which were published between 2003. These studies were conducted in various countries: Canada (n=7), China (n=3), the USA (n=2), and Ireland (n=1), encompassing data from a total of 11 countries, with one study including data from 46 countries. Among the 15 studies, one specifically addressed guidelines for Traditional Chinese Medicine (TCM)\u003csup\u003e[9]\u003c/sup\u003e, while the others focused on general CPGs. The studies aimed at different objectives: some developed concepts\u003csup\u003e[19-21, 25]\u003c/sup\u003e, others established models\u003csup\u003e[21]\u003c/sup\u003e, formulated frameworks\u003csup\u003e[3, 6, 7]\u003c/sup\u003e, and created measurement scales\u003csup\u003e[4, 9, 10, 22-24, 26]\u003c/sup\u003e. The development methods employed varied. Some studies relied solely on literature reviews\u003csup\u003e[3, 6, 19-21, 25]\u003c/sup\u003e, while others used expert opinion and consensus\u003csup\u003e[4, 24]\u003c/sup\u003e, qualitative research\u003csup\u003e[7]\u003c/sup\u003e, and a combination of literature review and expert consensus\u003csup\u003e[9, 10, 21-23, 26]\u003c/sup\u003e. Among the seven studies that developed scales, one either did not perform any assessments of reliability or validity\u003csup\u003e[9]\u003c/sup\u003e. Of those that did, some addressed both aspects\u003csup\u003e[10, 22-24]\u003c/sup\u003e, while others only evaluated one \u003csup\u003e[4, 26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Findings of the review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the included studies, 74 constructs were identified and classified (Supplement 2). These constructs were aggregated into 16 synthesised dimensions based on intrinsic meanings and overlapping content. After two rounds of discussions within the team, the first version of the CFGI was established. The content of the first version of CFGI included six domains and 38 constructs, namely clarity and consistency of recommendations (8 constructs), detail and usability (7 constructs), contextual relevance (4 constructs), implementation support (10 constructs), transparency/ authority and conflict of interest (6 constructs), feedback and updating procedures (3 constructs). The details of the findings from the literature for each domain in the first version of CFGI can be found in Supplement 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 External consultation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemi-structured focus group interviews were conducted via a video conferencing platform, involving a total of 16 experts from China (Table 2). Participants included clinicians, nurses, medical managers, and guideline methodologists from various regions across the country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Demographic characteristics of the interviewees (n=16)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)/ mean\u003c/strong\u003e\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Clinican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e11 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years) 27~43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e32.4\u0026plusmn;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Medical manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkplace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Guideline methodologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEast region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional title\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Junior title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Intermediate title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Advanced title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s or Doctoral degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e11 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Ethnic Han\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15 (93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Experience\u0026nbsp;\u003c/strong\u003e(years) 3~23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e10.4\u0026plusmn;5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOverall, respondents expressed strong support for the synthesized domains and constructs and provided revisions. All participants unanimously agreed that each domain, except for \u0026quot;transparency, authority, and conflict of interest,\u0026quot; significantly influences guideline implementability. For this domain, clinicians, in particular, indicated that they were less concerned about the funding sources and production processes of the guidelines, focusing instead on the origins of the recommendations.\u003c/p\u003e\n\u003cp\u003eRespondents discussed each construct, leading to a reduction in the number of constructs from 38 to 21. Thirteen constructs were removed due to vagueness, five were deemed unnecessary, and six were merged due to their similarities (see Supplement 4). Additionally, six new constructs were introduced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe criteria for removal emphasized flexibility, practicality, and traceability. Constructs that were too narrow, ambiguous, or overly similar were excluded. Detailed reasons for the inclusion or exclusion of each construct in the first version of the CFGI are provided in Supplement 4. For instance, the construct \u0026ldquo;The recommendations align with implementation objectives\u0026rdquo; was removed because \u0026ldquo;implementation objectives\u0026rdquo; can vary across contexts. Without specifying the objectives, this construct was too vague, and the term \u0026ldquo;align\u0026rdquo; lacked clarity regarding how or to what extent the recommendations were consistent with those objectives. In another example, the constructs \u0026ldquo;Recommendations are clear and unambiguous\u0026rdquo; and \u0026ldquo;The language of the guidelines is clear, simple, and concise\u0026rdquo; were merged, as they conveyed similar meanings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Final version of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCFGI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the external consultation, the final version of the CFGI comprised 21 constructs divided into six domains, refined through the natural language processing assistance and team discussion. Table 3 crosswalks the CFGI constructs related to implementability to 15 other frameworks. The final CFGI domains (Figure 3) and constructs are:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eScope and purpose (5 constructs)\u003c/strong\u003e: Guidelines should clearly describe the main content, scope of application, and target groups so that users can effectively apply the recommendations;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eClarity and consistency of recommendations (4 constructs)\u003c/strong\u003e: Emphasis is placed on clarity, applicability, implementation details, and support to ensure the effective implementation and adaptability of recommendations;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDevelopment and evidence base (5 constructs)\u003c/strong\u003e: It highlights the formation process of recommendations, selection criteria and quality of evidence to ensure ensure scientific reliability;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStructure and content (2 constructs)\u003c/strong\u003e: The rationality of the guidelines\u0026apos; layout and term definitions is crucial for user comprehension and application;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDevelopment team and transparency (3 constructs)\u003c/strong\u003e: Emphasizing stakeholder engagement, disclosure of potential conflicts of interest, and team integrity ensures the guidelines\u0026apos; impartiality and reliability;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eImplementation environment and tools (2 constructs):\u003c/strong\u003e guidelines should provide identifiable recommendations and a format that facilitates implementation.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA total of 29 experts completed the survey for external validation of CFGI (Table 4). The mean \u0026plusmn; SD ratings for rationality, importance, clarity, feasibility, and necessity across the six dimensions of the CFGI exceeded 4.0. The coefficient of variation (CV) was less than 0.25, indicating consistent evaluations among experts (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 4 Demographic characteristics of the experts (n=29)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Working\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e22 (75.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;5-10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e9 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;11-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e14 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;21~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking field\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e12 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Health services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e9 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e10 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Clinical medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e5 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 50~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e6 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Health statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e5 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Guideline methodology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e5 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e21 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Clinical pharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4 (13.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Clinical nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Health policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hospital management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e17 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eDoctoral degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e11 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNumerous clinical practice guidelines have been developed at considerable cost and effort\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, however, many remain unused or poorly implemented\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This paper presents the Comprehensive Framework for Guideline Implementability (CFGI) that consists of 21 constructs organized into six domains, developed through a systematic synthesis of reviews and expert consensus.\u003c/p\u003e \u003cp\u003eWe reflect on the CFGI development process and key decisions made. Starting with 16 dimensions from a systematic literature review, we created the first version with 6 domains and 38 constructs. Through expert consensus, we refined this to 21 constructs while improving clarity and logic. Ambiguous or redundant constructs were removed to avoid duplication and enhance structural coherence, while practical constructs were added to address real-world needs, such as improving the operability of recommendations and supporting clinical applications. These changes made the framework more relevant and applicable. For example, constructs in the \"clarity and consistency of recommendations\" domain now specify implementation steps more clearly, while the \"development process and evidence base\" domain emphasizes the link between evidence selection and recommendations. The final CFGI version, with 6 domains and 21 constructs, presents a streamlined, intuitive framework that provides guidance for guideline implementability.\u003c/p\u003e \u003cp\u003eAs a comprehensive framework, the CFGI harnesses many benefits of existing frameworks while generating its unique features. First, the CFGI is a comprehensive framework that offers a broader and more systematic taxonomy related to guideline implementability. Table\u0026nbsp;3 crosswalks the CFGI constructs related to implementability to 15 other frameworks, suggesting that many other frameworks miss some key constructs. For instance, proper formatting, layout, and structure, clearly defined key terms and definitions, and recommendations being feasible in clinical settings with specific implementation steps involved are all essential for implementation but are often missing in most frameworks. Second, the CFGI offers a logical and intuitive structure of domains, moving from the \"Scope and Purpose\" of the overall guideline, to the \"Clarity and Consistency of Recommendations,\" and then following with how those recommendations involve the \"Development and Evidence Base,\" their \"Structure and Content,\" and finally addressing the \"Development Team and Transparency\" behind them, culminating in the implementation environment and tools. A popular organization of domains in other frameworks follows \u0026ldquo;mediators\u0026rdquo; to implementability, such as \u0026ldquo;accessibility, communicability, and executability\u0026rdquo; in Jin Yinghui et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, and usability, adaptability, validity, applicability, etc., in Gagliardi et al.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. We chose to organize the domains by \u0026ldquo;content\u0026rdquo; that is empirically or theoretically linked to implementability. We believe organizing by content is more intuitive and specific, making assessment easier as well. Third, the CFGI is easier to apply and operationalize in assessments. Some constructs of the CFGI can be objectively assessed (presence or absence of the assessed constructs), such as \u0026ldquo;recommendations are supported by references\u0026rdquo; or \u0026ldquo;guideline presents the development team.\u0026rdquo; Others still require a subjective assessment but minimize that subjectivity by narrowing the assessment range. For example, \u0026ldquo;The guideline clearly describes the methods used to form the recommendations\u0026rdquo; allows relatively straightforward judgment on whether the method involved in developing the recommendations is \u0026ldquo;clear,\u0026rdquo; despite this being a somewhat subjective assessment. Finally, each constructs in the CFGI is empirically testable, laying the foundation for rigorous empirical testing of the validity of the CFGI to develop it into a scale in the future. We have planned an experiment based on a factorial trial wherein clinical practice guidelines will be adjusted to reflect the absence or presence or degree of the constructs involved in the CFGI, and then empirically testing whether those components actually lead to better implementability.\u003c/p\u003e \u003cp\u003e The CFGI serves distinct purposes compared to general guideline quality assessment. While quality evaluation of guidelines often examines overall scientific validity\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, the CFGI specifically focuses on assessing implementability, primarily at the individual recommendation level. It serves two main audiences: guideline developers and guideline users. For developers, the CFGI identifies fixable weaknesses during the guideline development process, allowing for modifications before finalization. For users, the framework assists in selecting more implementable guidelines and developing targeted implementation strategies to overcome identified obstacles. By providing insights into the factors affecting implementability, the CFGI empowers users to make informed decisions that enhance the likelihood of successful guideline adoption in practice.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe CFGI follows a robust, multi-step development process. It builds on existing theoretical frameworks for the implementability of clinical practice guidelines and incorporates stakeholder input. A comprehensive review of evaluation tools extracted relevant implementability content. Expert consensus refined the framework, while natural language processing and team discussions helped classify domains. The final version was externally validated at an international conference, gathering feedback from 29 experts. This meticulous process improved the framework's scientific rigor and its global applicability.\u003c/p\u003e \u003cp\u003eWe should note two limitations. First, while the framework has laid the foundation for quantifiable assessment, it has not yet been developed into a scale. Proper weighting and scoring methods need to undergo more rigorous psychometric evaluations. Second, although our constructs are based on theoretical foundations from the literature, these constructs still need to be empirically validated in terms of their ability to predict guideline implementation. However, as previously mentioned, we have planned a factorial trial to address this issue.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e Through a systematic review of existing frameworks and models, along with expert consultation, we developed the Comprehensive Framework for Guideline Implementability (CFGI) to systematically address factors affecting guideline implementation. The CFGI provides developers and users of clinical practice guidelines with tools to enhance guideline implementability. This framework emphasizes multi-stakeholder involvement and establishes a foundation for future validation studies aimed at improving guideline implementation and healthcare outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eEthics approval was obtained from the Institutional Review Board (IRB) of Southern Medical University in Guangzhou, China (# Southern Medical Audit (2024) No. 012). All participants provided informed consent prior to each interview and each survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData are available in a public.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interest. Dong (Roman) Xu, one of the authors of this paper, is an editor for Implementation Science Communications Journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was funded by the Swiss Agency for Development and Cooperation (# 81067392) and the National Natural Science Foundation of China (NSFC) (# 72304007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e \u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eXD conceived the project concept. XD, ZDM and Gregory reviewed and commented on the design and methods. ZDM developed the first draft, along with XD, Gregory and Alison. ZDM, WYM, LSS, YN, SZW, LZL and YLJ performed the literature screening, data extraction and data collection. XD, William, WYN, David, CK, CYL and ZPX reviewed the content and edited the manuscript. All coauthors participated in the revision and approved this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors thank all the interviewees and experts who contributed to this study by generously sharing their invaluable insights and experiences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTrustworthy I O M U, Guidelines C P. Clinical Practice Guidelines We Can Trust[M]. Washington (DC): National Academies Press (US), 2011.\u003c/li\u003e\n\u003cli\u003eDe Hert S, Paula-Garcia W N. Implementation of guidelines in clinical practice; barriers and strategies[J]. Curr Opin Anaesthesiol, 2024,37(2):155-162.\u003c/li\u003e\n\u003cli\u003eGagliardi A R, Brouwers M C, Palda V A, et al. How can we improve guideline use? A conceptual framework of implementability[J]. Implement Sci, 2011,6:26.\u003c/li\u003e\n\u003cli\u003eShiffman R N, Dixon J, Brandt C, et al. The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation[J]. BMC Med Inform Decis Mak, 2005,5:23.\u003c/li\u003e\n\u003cli\u003eZhou P, Chen L, Wu Z, et al. The barriers and facilitators for the implementation of clinical practice guidelines in healthcare: an umbrella review of qualitative and quantitative literature[J]. J Clin Epidemiol, 2023,162:169-181.\u003c/li\u003e\n\u003cli\u003eGupta S, Tang R, Petricca K, et al. The Guideline Language and Format Instrument (GLAFI): development process and international needs assessment survey[J]. IMPLEMENTATION SCIENCE, 2022,17(1).\u003c/li\u003e\n\u003cli\u003eKastner M, Estey E, Hayden L, et al. The development of a guideline implementability tool (GUIDE-IT): a qualitative study of family physician perspectives[J]. BMC Fam Pract, 2014,15:19.\u003c/li\u003e\n\u003cli\u003eBrouwers M C, Makarski J, Kastner M, et al. The Guideline Implementability Decision Excellence Model (GUIDE-M): a mixed methods approach to create an international resource to advance the practice guideline field[J]. Implement Sci, 2015,10:36.\u003c/li\u003e\n\u003cli\u003eXue B. The Establishment of Evaluation System for Clinical Practice Guidelines in Traditional Chinese Medicine and Research on Its Methods[D]. Beijing University of Chinese Medicine, 2021.\u003c/li\u003e\n\u003cli\u003eDevelopment and validation of an international appraisal instrument for assessing the quality of clinical practice guidelines: the AGREE project[J]. Qual Saf Health Care, 2003,12(1):18-23.\u003c/li\u003e\n\u003cli\u003eAromataris E M Z. JBI Manual for evidence synthesis[EB/OL]. [2023.11.14]. https://jbi-global-wiki.refined.site/space/MANUAL.\u003c/li\u003e\n\u003cli\u003eMoher D, Shamseer L, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement[J]. Syst Rev, 2015,4(1):1.\u003c/li\u003e\n\u003cli\u003eHennink M, Kaiser B N. Sample sizes for saturation in qualitative research: A systematic review of empirical tests[J]. Soc Sci Med, 2022,292:114523.\u003c/li\u003e\n\u003cli\u003eHennink M M, Kaiser B N, Marconi V C. Code Saturation Versus Meaning Saturation: How Many Interviews Are Enough?[J]. Qual Health Res, 2017,27(4):591-608.\u003c/li\u003e\n\u003cli\u003eLewinski A A, Crowley M J, Miller C, et al. Applied Rapid Qualitative Analysis to Develop a Contextually Appropriate Intervention and Increase the Likelihood of Uptake[J]. Med Care, 2021,59(Suppl 3):S242-S251.\u003c/li\u003e\n\u003cli\u003eThomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews[J]. BMC Med Res Methodol, 2008,8:45.\u003c/li\u003e\n\u003cli\u003eAverill J B. Matrix analysis as a complementary analytic strategy in qualitative inquiry[J]. 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Development and Validation of Clinical Practice Guideline Implementation Evaluation Tools[J]. Chinese Journal of evidence-based Medicine, 2022,01(22):111-119.\u003c/li\u003e\n\u003cli\u003eZhiKang Y, Gordon G. Standards for developing clinical practice guidelines: From the outside to the inside, How can clinical experts understand the inside of clinical guidelines[J]. Chinese general medicine, 2023:1-7.\u003c/li\u003e\n\u003cli\u003eXu D R, Cai Y, Wang X, et al. Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients[J]. Am J Public Health, 2022,112(6):913-922.\u003c/li\u003e\n\u003cli\u003eZhang L, Liang H, Luo H, et al. Quality in screening and measuring blood pressure in China\u0026apos;s primary health care: a national cross-sectional study using unannounced standardized patients[J]. Lancet Reg Health West Pac, 2024,43:100973.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1, 3 and 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Southern Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clinical practice guideline, implementability, framework, CFGI","lastPublishedDoi":"10.21203/rs.3.rs-6182899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6182899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e We define clinical practice guideline (CPGs) implementability as the characteristics of the guideline that reflect the extent to which it is likely to be adopted in clinical practice. Our objectives were to create a comprehensive and evidence-informed framework of guideline implementability (CFGI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eA mixed-methods approach was used. Based on a systematic literature review of six databases as the foundation, the initial version of the CFGI was created, followed by external consultations to gather feedback and natural language processing tool-assisted classificationto refine the framework. To get external validation of the CFGI from expert feedback at an international conference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 15 studies related to guideline implementability were identified from the systematic literature review. The first version of CFGI was compiled, including 6 domains. Feedback on the first version was received from 16 stakeholders, including clinicians, nurses, medical managers, and guideline methodologists, combined with natural language processing tool-assisted classification. The final version of the CFGI is comprised of 6 core domains, containing 21 constructs: (1) Scope and purpose; (2) Clarity and consistency of recommendations; (3) Development and evidence base; (4) Structure and Contents; (5) Development team and transparency; and (6) Implementation environment and tools. Twenty-nine experts participated in the external validation, and the results showed that CFGI had good rationality, importance, clarity, feasibility, and necessity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The development of the CFGI provides a systematic theoretical basis for the development and implementation of future CPGs, which will help to enhance the implementability of guidelines and facilitate their promotion and application in different medical settings. Future research can further validate and apply the CFGI, explore its effectiveness and feasibility in actual operation.\u003c/p\u003e","manuscriptTitle":"Implementability of Clinical Practice Guidelines: the Review and Development of a Comprehensive Framework for Guideline Implementability (CFGI)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 11:18:48","doi":"10.21203/rs.3.rs-6182899/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f616ea7-1c10-4155-843c-5fde36c5a456","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45575248,"name":"Preventive Medicine"}],"tags":[],"updatedAt":"2025-03-12T11:18:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 11:18:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6182899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6182899","identity":"rs-6182899","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}