Optimizing Audit and Feedback in Low-Resource Settings: A Best-Worst Scaling Study of Healthcare Workers Preference in Zanzibar

Optimizing Audit and Feedback in Low-Resource Settings: A Best-Worst Scaling Study of Healthcare Workers Preference in Zanzibar | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Audit and Feedback in Low-Resource Settings: A Best-Worst Scaling Study of Healthcare Workers Preference in Zanzibar Hamada Kidanga Mussa, Xiaoqing Zhu, Pramesh Koju, Huadan Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6270141/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Adherence to clinical guidelines is crucial for providing high-quality primary healthcare services. However, low- and middle-income countries (LMICs) like Zanzibar often face challenges adhering to the guidelines. Audit and feedback (A&F) mechanisms can play a significant role in identifying areas for improvement and promoting guideline compliance. Traditional feedback methods may not effectively prioritize the most impactful changes. Best-worst scaling (BWS) can provide a more nuanced approach to understanding preferences and priorities among healthcare practitioners. This study aims at optimizing feedback strategy by determining which components health workers prioritize. Methods This mixed-method study was done under two methodological approaches a) expert consultation and b) Best-worst scaling. Expert consultation was conducted to seek expert opinions on the various feedback components obtained from the literature. This was done through expert meetings and questionnaires. Three meetings were conducted and two rounds of questionnaires were distributed. A BWS survey was done to find the prioritized components from the primary healthcare workers. A series of questions was presented to them arranged in blocks to select the most important (best) and the least (worst) important components. Data were collected in an online platform called REDcap and analyzed using R software version 4.4.2 Results "Feedback with an improvement plan" was the highest-ranked component ( p < 0.001), "Feedback with goals/targets" was the second-ranked component ( p < 0.001), and "Delivery method of feedback" was the third-ranked feedback component ( p < 0.001). "Feedback with peer comparison", the "Format of the feedback report", and the "Source of feedback" had negative mean scores ( p = 0.0200, p = 0.0217, and p = 0.3408 respectively). "The Feedback recipient" component had the smallest mean score. This indicates that these are less important feedback components than others. Conclusion The findings from this study have found that the most preferred feedback components among health workers are "Feedback with an Improvement Plan," "Feedback with Goals/Targets," and "Delivery Method of Feedback". This suggests that they need feedback which is actionable, Goals oriented and well delivered. The findings therefore have given us ways to improve the technique we use to give feedback, enhance guidelines adherence, and, therefore, improve the quality of primary healthcare services. Audit and Feedback Best-worst Scaling Quality Primary Healthcare Evidence-based practice Figures Figure 1 Introduction Adherence to evidence-based clinical guidelines is crucial for providing high-quality Primary Healthcare (PHC) [ 1 – 3 ]. Following these guidelines helps ensure more consistent and accurate diagnoses and treatments, thereby minimizing adverse health outcomes. However, Low- and Middle-income Countries (LMICs) like Zanzibar often face challenges in adhering to these guidelines, resulting in suboptimal patient outcomes [ 1 , 3 ]. Audit and Feedback (A&F) is a widely used strategy to improve professional practice and patient outcomes. [ 4 , 5 ]. It involves summarizing data about specific aspects of practice and feeding it back to practitioners to encourage improving clinical performance [ 6 , 7 ]. However, A&F demonstrated mixed effectiveness in LMICs, mainly due to different practice contexts that require distinct configurations of A&F [ 8 ]. The accuracy of the audit is fundamental to delivering effective feedback. Audits can take many forms, including chart reviews, clinical vignettes, and exit interviews, and in recent years, the Unannounced Standardized Patient (USP) has been progressively used in primary health quality assessment, considering its high reliability and validity [ 9 ]. However, we still need to identify the optimal feedback configuration to unleash the potential of A&F that meets the low-resource context of LMICs, such as those in Tanzania. Zanzibar, a semi-autonomous region of Tanzania situated about fifteen miles off the coast of Tanzania mainland, exemplifies many challenges of using A&F in LMIC settings. For A&F to enhance engagement and Sustainability, feedback must align with healthcare providers' priorities—a critical gap in current practice, where top-down designs often neglect local realities. To address this, we applied Best-Worst Scaling (BWS), an innovative participatory method increasingly used in health research for user preferences but underutilized in LMICs compared to high-income countries [ 10 – 13 ]. Introduced by Finn and Louviere [ 14 , 15 ], BWS employs trade-off tasks grounded in Random Utility Theory (RUT) to quantify the relative importance of components within a set [ 10 , 16 ]. By asking participants to select the "best" and "worst" options iteratively, BWS identifies priorities that reflect real-world decision-making [ 17 , 18 ]. Hence, this study aimed to identify and rank the feedback components prioritized by Zanzibar's primary healthcare (PHC) providers to determine the most feasible and important feedback strategies for guideline adherence and quality improvement using the Best-Worst Scaling method. Methods Study Design and Setting We conducted this two-phase, mixed-methods study in PHC facilities across seven districts (North 'A', North' B', West 'A', West 'B', Urban, South, and Central districts) of Zanzibar, Tanzania, from June to December 2024. Phase 1 used a modified Delphi process to identify candidate A&F components, and Phase 2 employed Best-Worst Scaling to quantify healthcare worker preferences. The study adhered to STROBE guidelines for observational studies [ 19 ]. Zanzibar has a fair network of PHC facilities, which serve a population of about 3000 to 5000 per PHC facility, providing basic healthcare services (for dispensaries) and professional assistance with normal deliveries, laboratory services, and dental care (for Primary Healthcare Centers). PHCs represent the lowest level of the healthcare delivery framework, where prevention, health promotion, and disease management begin. Sampling method and sample size We used purposive sampling, with the research team selecting a list of experts based on their experience and working areas. The same method was used to obtain a representative sample of health workers for the BWS survey. Academicians, policymakers, health system administrators, and healthcare providers were invited to the consultation meetings and survey. The exclusion criterion was that the experts have no corresponding expertise or experience in the relevant field. The inclusion criteria for the survey included clinical officers, nurses, and other healthcare providers dealing with consultation, examination, diagnosis, and disease treatment in public PHC facilities. The exclusion criteria are interns or students who are working during the time of visits. We recruited two research assistants to distribute the questionnaire link. The sample size for the expert consultation, using the Delphi method, was determined based on the principle of saturation [ 20 ]. As there is no established consensus on the sample size for Delphi studies, we continued recruitment until no new significant insights or themes emerged from the expert meetings and questionnaires. Regarding the BWS survey, some guidelines exist, but there is no universally accepted standard for determining an adequate sample size [ 21 , 22 ]. Previous research using BWS has employed sample sizes ranging from 15 to 803, with a median of 175 [ 23 ]. Considering the sample size used in most studies, we aimed to recruit 80 primary healthcare workers (one from each facility) to complete the formal survey. This sample represented over 50% of Zanzibar's primary healthcare facilities [ 24 ]. Data collection Data were collected using both qualitative and quantitative methods, which involved two main stages. All data for this study were collected and managed through the REDCap system. The Redcap is a free and powerful cloud tool for data collection, storage, and management. Expert Consultation (Experts meetings and the Delphi method) to identify candidate feedback components We conducted a comprehensive literature review to identify potential feedback components for guideline adherence in PHC settings. Based on the findings, we refined the components and selected the most important and feasible ones. Fourteen components and their levels ( additional file 1 ) were presented to a purposive sample of experts and stakeholders with different backgrounds to avoid bias [ 25 ] during three meetings to discuss their feasibility and significance within the local context of Zanzibar. These open meetings allowed experts to reflect and engage in discussions with researchers. We introduced the study using PowerPoint slides outlining potential feedback components, explaining the experts' tasks of evaluating the components' importance and feasibility before selecting the seven most crucial ones. The meetings, facilitated by the principal investigator, lasted approximately 40 to 60 minutes and were recorded and conducted offline. Subsequently, online questionnaires were distributed anonymously to the experts following the Delphi method to create a clear and distinguishable list of feedback intervention components [ 26 ]. The Delphi method is a structured approach that facilitates group communication, enabling experts to address complex problems more effectively [ 27 ] with the assumption that group consensus yields more meaningful results than individual judgments, regardless of individual expertise [ 28 ]. The Delphi process requires at least two rounds of consultations with the same experts and a fixed set of questions to reach a consensus [ 29 ]. Therefore, we conducted a second round of expert questionnaires, and the results were uniformly entered into REDCap via the distributed online questionnaire link. A summary of the meetings and results of the first round of online surveys was prepared. In an online survey, the experts were asked to select one option given for each of the fourteen components to rank the component importance. The choices for components' importance were “Not Important at all, Slightly Important, Moderately Important, Important” and “Extremely Important”. Moreover, the options for the components’ feasibility were “Not Feasible, Slightly Feasible (with many constraints), Moderately Feasible (with some constraints)”, and “Highly Feasible”. After the first round of questionnaires, feedback was given to experts so that they could assess their opinions with the rest of the others, hence reaching a consensus for the second round. Best-worst scaling (BWS) to rank feedback components BWS is a survey method to elicit individual preferences [ 17 , 30 ] that has three common cases (types): the object case, profile case, and Multi-profile cases [ 15 , 22 , 31 , 32 ]. We employed the object case to rank feedback components, as this approach is optimal for prioritizing discrete attributes rather than evaluating multi-attribute profiles. BWS requires participants to select the most (best) and least (worst) preferred components in repeated choice tasks [ 10 , 33 ]. One advantage of using BWS is that it gives us an understanding of the "best and worst" choice for respondents, unlike conventional Discrete Choice Experiment (DCE) where respondents can only choose the "best" [ 17 , 34 ]. After selecting the top seven feedback components from the Delphi process, the BWS survey was employed to rank and identify three potentially most effective and feasible components. The BWS survey used the Balanced Incomplete Block Design (BIBD) with R software version 4.4.2 [ 32 ] ( additional file 2 ). BIBD organizes items into blocks and balances the number of items presented across participants, which allows for efficient comparisons among a set of items, reducing bias and increasing the statistical precision of the ratings [ 10 , 22 , 34 ]. In our study, the seven feedback components were organized into seven different blocks, each containing three A&F components, with each component appearing three times in the questionnaire [ 32 ]. We explained and provided examples for each component so respondents could understand its meaning clearly [ 22 ]. We invited participants to reflect on which component of these seven blocks is most effective (most important) and which is least effective (least important). We assigned 1 point when a component was chosen as most effective (best) and − 1 when a component was selected as least effective (worst). The results from the BWS survey were then used to finalize the three most effective components. Table 1 shows an example of a BWS block question implemented through REDcap. Table 1 An Example of a BWS Questionnaire Configuration file To improve the quality of care, we plan to organize an intervention to measure and evaluate key health quality indicators for practitioners (completion rate of key consultation and examination items, diagnosis and treatment, etc. specified in medical guidelines) and then provide feedback to practitioners on the evaluation results, i.e., an "Audit and Feedback" intervention. Now, you will see some multiple-choice questions containing three items for the "Feedback" component in blocks. Which feedback components (factors) do you consider the MOST IMPORTANT in promoting guidelines adherence in managing diseases? And which one is LEAST IMPORTANT? Most important (Best) Feedback Component Explanation with examples Least important (Worst) Feedback with Peer Comparison The feedback report will include feedback on the health worker's clinical performance and comparisons with peers and other health facilities. For example, your current adherence rate to managing diabetes as per the NCD protocol is 60%, whereas Kidoti dispensary's is 75%, and Matemwe dispensary's is 80%. Feedback with Goals/Targets In addition to feedback on the health worker's clinical performance, the feedback report will include the goals/targets to be achieved regarding clinical performance for the management of hypertension and diabetes within a certain period. For example, your current adherence rate to standard diabetes management as per the NCD protocol is 60%. You have to increase it to 80% in the next 3 months. Format of the Feedback Report The format of the Feedback Report means that the feedback is organized in a certain specific way. This can be; "Textual report", in which the feedback report is in text format only. For example, the feedback would be a line like "Your hypertension diagnosis counseling completion rate is 60%". "Feedback report designed with texts and visual displays". This means that the feedback report is in text and visual display formats. For example, bar graphs and charts show how well healthcare professionals are implementing hypertension and diabetes guidelines in the clinical setting. After developing the BWS questionnaires, we conducted a pilot survey involving ten participants. Verbal informed consent was obtained before the pilot survey. After completing the BWS questionnaires (average time: 15 minutes), participants were invited to provide feedback on the questionnaires. Based on their suggestions, we refined the questionnaires for the formal survey. Data analysis The statistical analysis for the expert consultations was performed using Microsoft Excel/IBM SPSS 25.0. Approaches were used to analyze expert consultation data including the expert Authority (Cr) for the Delphi method, Threshold Method, and Kendall's Concordance coefficient. Expert Authority (Cr) for the Delphi method: The expert authority coefficient is an important factor used to judge the validity of consultation results. The expert authority coefficient (Cr) was determined by two (2) factors: the expert's judgment basis on the consultation items (Ca) and the expert's degree of familiarity with the items (Cs). (Cr)=(Ca + Cs)/2. High Cr values (> 0.7) indicate a high degree of expert authority [ 35 ]. Threshold Method: This involves setting a threshold based on the mean scores for importance, the rate of full marks, and the degree of variation. Indicators that fall below or exceed this threshold are removed [ 35 , 36 ]. Kendall's coefficient: The number of consultation rounds can be decided based on Kendall's coefficient, which requires stopping when Kendall's coefficient exceeds 0.7. It was used to measure the agreement of the experts’ opinions. The mean score for each item's importance and the coefficient of variation (CV) evaluated the experts’ opinions. R Software, version 4.4.2, was used to analyze the BWS data through two approaches; counting and modeling. First, we conducted a count analysis to assess the frequency of each feedback component's selection. The Best-Worst Scaling (BW) score was calculated by subtracting the number of times a component was selected as the 'least' important from the number of times it was chosen as the 'most' important. Next, we applied the conditional logit model in our modeling approach. The best-worst mean score determined each component's importance, ranked from largest to smallest. A score near + 1.0 indicates greater importance, while a score near − 1.0 suggests lesser importance. We calculated the frequency of each component selected as best or worst, along with a 95% confidence interval, enabling us to identify the three most important components. Ethical approval This study is a part of an Audit and Feedback study in Zanzibar, Tanzania approved by The Zanzibar Health Research Institute (ZAHRI) through its Zanzibar Health Research Ethical Committee (ZAHREC) with ref: No. ZAHREC/05/ST/AUG/2023/149), with Government approval (Permit No. Ref: 2001710219233839296653). Results Participant Demographics for expert meetings Nine experts participated in the Delphi process ( Table 2 ). The majority (77.8%) were male, with a mean age of 44.11 years. Most experts (77.8%) held a master's degree, and the mean professional experience was 11.78 years (SD = 8.09). Table 2 Expert Meeting participant demographics Item n Percentage (%) Mean ± Standard deviation Gender Male 7 77.8 Female 2 22.2 Age (years) 44.11 ± 9.52 Educational background Diploma 1 11.1 Bachelor degree 1 11.1 Master's degree 7 77.8 Years of professional experience 11.78 ± 8.09 Work title Academicians 2 22.2 Researchers 3 33.3 Administrators 3 33.3 Medical doctor 1 11.1 Delphi analysis Expert enthusiasm The effective recovery rate of expert consultation questionnaires from both rounds of expert consultation was 100.0%, and the proportion of expert suggestions was 100.0%. Expert Authority (Cr) The expert authority coefficient (Cr) was 0.848 for the first round and 0.967 for the second round, indicating a high degree of expert authority. Components selection After the first round, nine components met the threshold requirements to be selected (SD = 0.706). The Kendall's coefficient for the first round was 0.443 ( p < 0.001). Following the second round, Kendall's coefficient increased to 0.803 (p < 0.001). After two rounds of expert consultation and based on the research interest of the research team seven final components and their levels were selected for the next step of the BWS questionnaire (Feedback with clinical guidelines was deleted and Feedback with Peer comparison was chosen because of the interest of the research team). See table 3 below; Table 3 Components selected for the BWS questionnaire Component's name Component's levels 1. Feedback with an improvement plan Yes No 2. Feedback with goals/targets Yes No 3. Source of Feedback Authoritative Body Researcher 4. Delivery Method of Feedback Report Face to Face Phone calls 5. Feedback Recipients Authoritative body Health worker 6. Format of the Feedback Report Text only Text with graphs 7. Feedback with Peer comparison Yes No Best-worst scaling analysis results Participant Demographics Of 80 targeted healthcare facilities, 77 (96%) responded to the survey. Four responses were excluded for quality issues, and three facilities (3.75%) were unavailable due to construction work. In the end, 73 (91.25%) provided usable data ( Table 4 ). The respondents are gender-balanced, mostly diploma holders (89.04%) working as clinical officers (71.23%) in dispensaries (66%), averaging 7.5 years of healthcare experience. Table 4 Background information of participants for the BWS survey Item n Percentage (%) Mean ± Standard deviation Gender Male 34 46.58 Female 39 53.42 Age (years) 33.23 ± 7.84 Educational background Diploma 65 89.04 Advanced Diploma 3 4.11 Bachelor degree 5 6.85 Years of experience in healthcare 7.55 ± 7.61 Professional field Medicine 57 78.08 Pharmacy 3 4.11 Nursing 12 16.44 Health technology 1 1.37 Designation Medical Doctor 5 6.85 Clinical officer 52 71.23 Others 16 21.92 Years of Experience in Managing Hypertension and Diabetes 6.6 ± 7.37 Type of Health Facility Primary health Care center 25 34.25 Dispensary 48 65.75 Counting approach for BWS The three most important components identified were "Feedback with Improvement Plan", "Feedback with Goals/targets", and the "Delivery method of feedback" (table 5 and Figure 1). The error bars indicate a 95% confidence interval. Table 5 Feedback components scores Component B W BW Rank meanB meanW meanBW mean.stdBW sqrtBW Comparison 59 111 -52 4 0.8082 1.5205 -0.7123 -0.2374 0.7291 Goals 125 15 110 2 1.7123 0.2055 1.5068 0.5023 2.8868 Plan 147 18 129 1 2.0137 0.2466 1.7671 0.5890 2.8577 Delivery 87 55 32 3 1.1918 0.7534 0.4384 0.1461 1.2577 Format 34 87 -53 5 0.4658 1.1918 -0.7260 -0.2420 0.6251 Source 32 107 -75 6 0.4384 1.4658 -1.0274 -0.3425 0.5469 Recipients 27 118 -91 7 0.3699 1.6164 -1.2466 -0.4155 0.4783 From figure 1 , a score close to +1.0 indicates that the element is relatively more important, while a score close to -1.0 suggests the opposite. Modeling approach The modeling approach yielded the same top three components ( Table 6 ). "Feedback with Peer Comparison", "Format of the Feedback Report," and "Source of Feedback" had negative mean scores (p = 0.0200, p = 0.0217, and p = 0.3408, respectively). "Feedback Recipient" had the smallest mean score. Table 6 Results from the Modeling Approach Coefficients Exponential coefficient Standard error z p Plan 1.6541 5.2284 0.1441 11.478 < 0.001* Goals 1.4791 4.3888 0.1382 10.703 < 0.001* Source 0.1173 1.1245 0.1232 0.953 0.3408 Delivery 0.8758 2.4008 0.1258 6.962 < 0.001* Comparison 0.2846 1.3292 0.1224 2.326 0.0200* Format 0.2810 1.3245 0.1224 2.296 0.0217* Recipients [1] NA NA 0.0000 NA NA *Indicates statistically significant. Preference heterogeneous Male participants prioritized feedback delivery methods more highly than females, while other preferences remained similar between genders ( additional file 3 ). Discussion This study aimed to identify the feedback components that healthcare workers prioritize to enhance guideline adherence and quality of care in Zanzibar's primary healthcare settings. Our findings highlight that "Feedback with an Improvement Plan," "Feedback with Goals/Targets," and "Delivery Method of Feedback" are the most valued components by PHC workers. While audit and feedback is a widely used and important strategy to improve adherence to best practices, few studies have explored primary healthcare workers' preferences for feedback modalities in LMICs. Thus, our study provides valuable insights for policymakers and healthcare organizations, emphasizing the need to tailor feedback mechanisms to the local context for quality improvement efforts. These preferences align with the understanding that actionable feedback linked to concrete action plans [7], and specific goals [37,][38] is most effective in driving behavior change and professional development [39]. This might indicate that health workers need clear actionable and goal-oriented feedback that address the areas for improvement. This will more likely leads to behavior change among health worker hence improving the quality of health services they deliver. In low resource settings like Zanzibar, this can give a roadmap to overcome some challenges in healthcare delivery. The goal oriented feedbacks can help to prioritize tasks and therefore improve efficiency in resource utilization. Additionally, the importance of delivery method echoes previous findings that feedback from a supervisor or respected colleague can be particularly impactful [40,][41]. This highlights the need to understand the need for feedback to be communicated in a way that will encourage health workers to act in a constructive manner. In low resource settings like Zanzibar where interpersonal relationship varies, it is important to consider how feedback is delivered to enhance the overall quality of the services. While our findings resonate with existing literature on feedback effectiveness, some discrepancies were also observed. Notably, the "Source of Feedback" was not prioritized by PHC workers in our study, contrasting with previous research suggesting that feedback from a trusted source is more impactful [7]. This difference might be attributed to the specific context of Zanzibar's PHC system where authority figures may not always be perceived as supportive or accessible. Alternatively, the characteristics of our study sample, which consisted predominantly of clinical officers and nurses with diploma-level education, might have influenced these preferences. Many previous studies focused on stakeholders in general and not health workers alone. Likewise, the source was mentioned in our study as an important component in the experts consultation but not in the BWS survey with primary health workers. This study found that "Feedback with peer comparison" was not a feedback preference for guideline adherence among PHC workers in Zanzibar. This is similar to some previous studies [42], which suggested that "peer comparison in primary care" was ineffective and reduced job satisfaction. Health workers do not like the to be compared with others. They need to feel the sense of independence in their daily activities. Therefore, feedback should focus on individual growth and development rather than comparison to others. However, this contradicts another recent study, a secondary analysis of a randomized clinical trial [43]. Further research is needed to explore these contextual variations and their implications for feedback design. Interestingly, our results diverge from findings in high-income countries, where peer comparison and data-driven feedback are often emphasized [44,][45]. This underscores the importance of considering contextual factors when designing feedback interventions in LMICs. Resource constraints, cultural norms, and existing healthcare infrastructure can all influence the feasibility and effectiveness of different feedback modalities. Results from this study show how males and females perceive different feedback components as influencers to guidelines adherence. For example, the “Delivery method of feedback” shows higher positive scores for males than females. This may indicate that men have higher priorities in certain communication styles which can influence their behavior than women. The positive scores indicate that feedback components align with the preferences of both genders [46]. Strengths and Limitations A key strength of this study lies in its innovative application of Best-Worst Scaling (BWS) to quantify preferences within a resource-constrained setting. BWS distinguishes itself from conventional surveys by forcing clear best-worst choices rather than neutral responses common in the Likert scale, reducing social desirability bias (tendency to agree), providing better item differentiation, and mirroring real-world decisions. To our knowledge, this is the first BWS study to prioritize A&F components in sub-Saharan Africa, addressing the need for participatory methods in LMIC health systems research. Additionally, the mixed-methods design, incorporating qualitative stakeholder consultation for consensus-building alongside the BWS survey, further strengthen the study's validity and applicability to local contexts. However, we also acknowledge several limitations. First, the prioritization of attributes was limited to those selected through the Delphi process; other factors influencing guideline adherence may exist and warrant consideration in future research. Second, while BWS effectively simulates real-world trade-offs, actual behavior may not always align with stated preferences. Future research, which we have planned, will examine the effectiveness of these prioritized components in real-world A&F interventions. Finally, contextual differences may limit the direct generalizability of our findings to other LMIC settings. Nonetheless, the insights from this study in a sub-Saharan African context may be more relevant to similar settings than findings from developed countries. Despite these limitations, our study provides insights for optimizing feedback strategies in resource-constrained settings. Recommendations Healthcare organizations and policymakers in Zanzibar and similar contexts should prioritize feedback mechanisms that include clear improvement plans and goal setting, while also considering the most appropriate delivery methods for their workforce. Further research is needed to evaluate the effectiveness of these strategies in improving guideline adherence and patient outcomes. Conclusion Using Best-Worst Scaling as part of the audit and feedback strategy has promising results for improving guideline adherence in primary healthcare settings within low- and middle-income countries like Zanzibar. The findings from this study have found that the most preferred feedback components among health workers are "Feedback with an Improvement Plan," "Feedback with Goals/Targets," and "Delivery Method of Feedback". The sample size is enough for the results to be generalized in Zanzibar and similar settings. Healthcare organizations (the Ministry of Health) should establish structured feedback processes incorporating improvement plans and goal-setting as central elements. Further research should focus on testing these feedback components in real work environments using audit and feedback interventions. Similarly, future studies may need to increase the number of feedback components to be evaluated by health workers. Moreover, we recommend a qualitative study to get a deeper knowledge of facilitators and barriers to the feedback components. List of abbreviations A&F Audit and feedback BIBD Balanced Incomplete Block Design BWS Best worst Scaling DCE Discrete Choice Experiment EBP Evidence-based practice LMICs Low- and middle-income countries PI Principal Investigator PHC Primary Healthcare REDCap Research Electronic Data Capture RUT Random Utility Theory SDGs Sustainable Development Goals STROBE STrengthening the Reporting of OBservational studies in Epidemiology. UHC Universal Health Coverage USP Unannounced Standardized Patient ZAHREC Zanzibar Health Research Ethical Committee ZAHRI Zanzibar Health Research Institute Declarations Ethics approval and consent to participate This study is a part of an Audit and Feedback study in Zanzibar which was reviewed and approved by The Zanzibar Health Research Institute (ZAHRI) through its Zanzibar Health Research Ethical Committee (ZAHREC) with Ref: NO. ZAHREC/05/ST/AUG/2023/149 and got Government approval with permit no. Ref: 2001710219233839296653. Before the study, we sent electronic/paper-based materials, including an informed consent form, to the participants. The study did not start until the participants' informed consent forms were obtained. Consent for publication Not applicable Availability of data and materials Data is available upon request Competing interests The authors declare no competing interest. Funding National Natural Science Foundation of China (72404118), China Postdoctoral Science Foundation (2023M731536), Guangdong Medical Science and Technology Research Foundation (A2023133), and Swiss Agency for Development and Cooperation (81067392). Authors' contributions Conceptualization—DX; Methodology – HK, XZ & HL; Data Curation: XZ & HK; Formal analysis – HK & XZ; Writing of Original draft – HK; Writing review & editing – HK, HH, PK & XZ; Supervision & Validation –DX & HL Acknowledgment We acknowledge the funding from the National Natural Science Foundation of China (72404118), China Postdoctoral Science Foundation (2023M731536), Guangdong Medical Science and Technology Research Foundation (A2023133), and Swiss Agency for Development and Cooperation (81067392). We also acknowledge the Revolutionary Government of Zanzibar for approving the research project. Our special thanks to all healthcare workers whose active participation was crucial to the progress of my project. Authors' information (optional) 1. School of Health Management, Southern Medical University, Guangzhou, China 2. School of Health and Medical Sciences, the State University of Zanzibar 3. School of Public Health, Southern Medical University, Guangzhou, China 4. Pharmacovigilance Unit / Department of Public Health and Community Programs, Dhulikhel Hospital, Kathmandu University Hospital, Dhulikhel, Nepal 5. SMU Institute for Global Health (SIGHT) and Center for World Health Organization Studies, School of Health Management and Dermatology Hospital of Southern Medical University (SMU), Guangzhou, China 6. Acacia Lab for Implementation Science, School of Health Management and Dermatology Hospital, Southern Medical University, Guangzhou, China # Contributed equally to this work * Corresponding authors: 1) Prof. Dong (Roman) Xu. Email: [email protected] 2) Huanyuan Luo, Email. [email protected] References Wiedenmayer, K., Ombaka, E., Kabudi, B., Canavan, R., Rajkumar, S., Chilunda, F., Sungi, S., & Stoermer, M. (2021). Adherence to standard treatment guidelines among prescribers in primary healthcare facilities in the Dodoma region of Tanzania. BMC Health Serv Res, 21(1), 272. https://doi.org/10.1186/s12913-021-06257-y Adams, O. P., & Carter, A. O. (2010). Diabetes and hypertension guidelines and the primary health care practitioner in Barbados: knowledge, attitudes, practices and barriers--a focus group study. BMC Fam Pract, 11, 96. https://doi.org/10.1186/1471-2296-11-96 Liu, X. L., Wang, T., Tan, J. Y., Stewart, S., Chan, R. J., Eliseeva, S., Polotan, M. J., & Zhao, I. (2022). Sustainability of healthcare professionals' adherence to clinical practice guidelines in primary care. BMC Prim Care, 23(1), 36. https://doi.org/10.1186/s12875-022-01641-x Ivers, N., Jamtvedt, G., Flottorp, S., Young, J. M., Odgaard-Jensen, J., French, S. D., O'Brien, M. A., Johansen, M., Grimshaw, J., & Oxman, A. D. (2012b). Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev(6), CD000259. https://doi.org/10.1002/14651858.CD000259.pub3 Hysong, S. J., Best, R. G., & Pugh, J. A. (2006). Audit and feedback and clinical practice guideline adherence: making feedback actionable. Implement Sci, 1, 9. https://doi.org/10.1186/1748-5908-1-9 Brown, B., Gude, W. T., Blakeman, T., van der Veer, S. N., Ivers, N., Francis, J. J., Lorencatto, F., Presseau, J., Peek, N., & Daker-White, G. (2019). Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci, 14(1), 40. https://doi.org/10.1186/s13012-019-0883-5 Colquhoun, H. L., Carroll, K., Eva, K. W., Grimshaw, J. M., Ivers, N., Michie, S., & Brehaut, J. C. (2021). Informing the research agenda for optimizing audit and feedback interventions: results of a prioritization exercise. BMC Med Res Methodol, 21(1), 20. https://doi.org/10.1186/s12874-020-01195-5 Meherali, S., Punjani, N. S., & Mevawala, A. (2020). Health Literacy Interventions to Improve Health Outcomes in Low- and Middle-Income Countries. Health Lit Res Pract, 4(4), e251-e266. https://doi.org/10.3928/24748307-20201118-01 Xu, D. R., Cai, Y., Wang, X., Chen, Y., Gong, W., Liao, J., Zhou, J., Zhou, Z., Zhang, N., Tang, C., Mi, B., Lu, Y., Wang, R., Zhao, Q., He, W., Liang, H., Li, J., & Pan, J. (2022). Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients. Am J Public Health, 112(6), 913-922. https://doi.org/10.2105/ajph.2022.306779 Schuster, A. L. R., Crossnohere, N. L., Campoamor, N. B., Hollin, I. L., & Bridges, J. F. P. (2024). The rise of best-worst scaling for prioritization: A transdisciplinary literature review. Journal of Choice Modelling, 50. https://doi.org/10.1016/j.jocm.2023.100466 Landis-Lewis, Z., Kononowech, J., Scott, W. J., Hogikyan, R. V., Carpenter, J. G., Periyakoil, V. S., Miller, S. C., Levy, C., Ersek, M., & Sales, A. (2020). Designing clinical practice feedback reports: three steps illustrated in Veterans Health Affairs long-term care facilities and programs. Implement Sci, 15(1), 7. https://doi.org/10.1186/s13012-019-0950-y Wright-Hughes, A., Willis, T. A., Wilson, S., Weller, A., Lorencatto, F., Althaf, M., Seymour, V., Farrin, A. J., Francis, J., Brehaut, J., Ivers, N., Alderson, S. L., Brown, B. C., Feltbower, R. G., Gale, C. P., Stanworth, S. J., Hartley, S., Colquhoun, H., Presseau, J.,…Foy, R. (2022). A randomised fractional factorial screening experiment to predict effective features of audit and feedback. Implement Sci, 17(1), 34. https://doi.org/10.1186/s13012-022-01208-5 Quigley, M., Earnest, A., Andrikopoulos, S., Wischer, N., Green, S., & Zoungas, S. (2022). ANDA-Evaluating Facilitated Feedback Enhancement - a Cluster randomised Trial (ANDA-EFFECT): protocol for a cluster randomised trial of audit feedback augmented with education and support, compared to feedback alone, on acceptability, utility and health outcomes in diabetes centres in Australia. Trials, 23(1), 976. https://doi.org/10.1186/s13063-022-06910-9 Shoji, Y., Kim, H., Kubo, T., Tsuge, T., Aikoh, T., & Kuriyama, K. (2021). Understanding preferences for pricing policies in Japan’s national parks using the best–worst scaling method. Journal for Nature Conservation, 60. https://doi.org/10.1016/j.jnc.2021.125954 Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best--worst scaling: What it can do for health care research and how to do it. J Health Econ, 26(1), 171-189. https://doi.org/10.1016/j.jhealeco.2006.04.002 Hall, R., Medina-Lara, A., Hamilton, W., & Spencer, A. (2022). Women's priorities towards ovarian cancer testing: a best-worst scaling study. BMJ Open, 12(9), e061625. https://doi.org/10.1136/bmjopen-2022-061625 Potoglou, D., Burge, P., Flynn, T., Netten, A., Malley, J., Forder, J., & Brazier, J. E. (2011). Best-worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med, 72(10), 1717-1727. https://doi.org/10.1016/j.socscimed.2011.03.027 Wittenberg, E., Bharel, M., Bridges, J. F., Ward, Z., & Weinreb, L. (2016). Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women. Ann Fam Med, 14(4), 359-364. https://doi.org/10.1370/afm.1937 von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet, 370(9596), 1453-1457. https://doi.org/10.1016/s0140-6736(07)61602-x Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science & medicine, 292, 114523. Louviere J, F. T., Marley A. (2015). Best-Worst Scaling: Theory, Methods and Applications. Cambridge, United Kingdom: Cambridge University Press. Xu, R. H., Zhou, L., Wong, E. L., Wang, D., Xiang, G. C., & Xu, C. (2020). A best-worst scaling survey of medical students' perspective on implementing shared decision-making in China. BMC Med Educ, 20(1), 486. https://doi.org/10.1186/s12909-020-02406-9 Cheung, K. L., Wijnen, B. F., Hollin, I. L., Janssen, E. M., Bridges, J. F., Evers, S. M., & Hiligsmann, M. (2016). Using Best-Worst Scaling to Investigate Preferences in Health Care. Pharmacoeconomics, 34(12), 1195-1209. https://doi.org/10.1007/s40273-016-0429-5 Spencer, M., Greville‐Heygate, F., Singer, R., Rees, J., & MacDonagh, R. (2015). Health Improvement Project Zanzibar: a new model of healthcare delivery. Trends in Urology & Men's Health, 6(5), 35-39. https://doi.org/10.1002/tre.485 Garnett, C., Crane, D., West, R., Brown, J., & Michie, S. (2015). Identification of Behavior Change Techniques and Engagement Strategies to Design a Smartphone App to Reduce Alcohol Consumption Using a Formal Consensus Method. JMIR Mhealth Uhealth, 3(2), e73. https://doi.org/10.2196/mhealth.3895 Gattrell, W. T., Logullo, P., van Zuuren, E. J., Price, A., Hughes, E. L., Blazey, P., Winchester, C. C., Tovey, D., Goldman, K., Hungin, A. P., & Harrison, N. (2024). ACCORD (ACcurate COnsensus Reporting Document): A reporting guideline for consensus methods in biomedicine developed via a modified Delphi. PLoS Med, 21(1), e1004326. https://doi.org/10.1371/journal.pmed.1004326 Nasa, P., Jain, R., & Juneja, D. (2021). Delphi methodology in healthcare research: How to decide its appropriateness. World J Methodol, 11(4), 116-129. https://doi.org/10.5662/wjm.v11.i4.116 Niederberger, M., & Spranger, J. (2020). Delphi Technique in Health Sciences: A Map. Front Public Health, 8, 457. https://doi.org/10.3389/fpubh.2020.00457 Zhai, X. D., Wang, C. X., Ma, Y. J., Yu, J. X., Xiang, S. S., Jiao, H. Y., Shao, P., Guan, X., Wang, J., & Zhang, H. Q. (2020). Using the modified Delphi method to research the influencing factors of long-term health-related quality of life in patients with unruptured intracranial aneurysms after endovascular treatment. Chin Neurosurg J, 6, 7. https://doi.org/10.1186/s41016-020-00186-1 Luo, N., Uy, E. J. B., Bautista, D. C., Xin, X., Cheung, Y. B., Thio, S.-T., & Thumboo, J. (2018). Using best-worst scaling choice experiments to elicit the most important domains of health for health-related quality of life in Singapore. Plos One, 13(2). https://doi.org/10.1371/journal.pone.0189687 Muhlbacher, A. C., Kaczynski, A., Zweifel, P., & Johnson, F. R. (2016). Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev, 6(1), 2. https://doi.org/10.1186/s13561-015-0079-x Aizaki, H., & Fogarty, J. (2023). R packages and tutorial for case 1 best–worst scaling. Journal of Choice Modelling, 46. https://doi.org/10.1016/j.jocm.2022.100394 Mühlbacher, A. C., Zweifel, P., Kaczynski, A., & Johnson, F. R. (2016). Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. Health Econ Rev, 6(1), 5. https://doi.org/10.1186/s13561-015-0077-z Hollin, I. L., Paskett, J., Schuster, A. L. R., Crossnohere, N. L., & Bridges, J. F. P. (2022). Best-Worst Scaling and the Prioritization of Objects in Health: A Systematic Review. Pharmacoeconomics, 40(9), 883-899. https://doi.org/10.1007/s40273-022-01167-1 Zhao, Y., He, L., Hu, J., Zhao, J., Li, M., Huang, L., Jin, Q., Wang, L., & Wang, J. (2022). Using the Delphi method to establish pediatric emergency triage criteria in a grade A tertiary women's and children's hospital in China. BMC Health Serv Res, 22(1), 1154. https://doi.org/10.1186/s12913-022-08528-8 Shi, C., Zhang, Y., Li, C., Li, P., & Zhu, H. (2020). Using the Delphi Method to Identify Risk Factors Contributing to Adverse Events in Residential Aged Care Facilities. Risk Manag Healthc Policy, 13, 523-537. https://doi.org/10.2147/rmhp.S243929 Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. Am Psychol, 57(9), 705-717. https://doi.org/10.1037//0003-066x.57.9.705 Lloyd, R., Munro, J., Evans, K., Gaskin-Williams, A., Hui, A., Pearson, M., Slade, M., Kotera, Y., Day, G., Loughlin-Ridley, J., Enston, C., & Rennick-Egglestone, S. (2023). Health service improvement using positive patient feedback: Systematic scoping review. Plos One, 18(10), e0275045. https://doi.org/10.1371/journal.pone.0275045 Rapin, J., Gendron, S., Mabire, C., & Dubois, C. A. (2023). Feedback on clinical team performance: how does it work, in what contexts, for whom, and for what changes? A critical realist qualitative multiple case study. BMC Health Serv Res, 23(1), 410. https://doi.org/10.1186/s12913-023-09402-x Ivers, N. M., Grimshaw, J. M., Jamtvedt, G., Flottorp, S., O'Brien, M. A., French, S. D., Young, J., & Odgaard-Jensen, J. (2014). Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med, 29(11), 1534-1541. https://doi.org/10.1007/s11606-014-2913-y Hattie, J. A. C., & Timperley, H. S. (2007). The Power of Feedback. Review of Educational Research, 77, 112 - 181. Reiff, J. S., Zhang, J. C., Gallus, J., Dai, H., Pedley, N. M., Vangala, S., Leuchter, R. K., Goshgarian, G., Fox, C. R., Han, M., & Croymans, D. M. (2022). When peer comparison information harms physician well-being. Proc Natl Acad Sci U S A, 119(29), e2121730119. https://doi.org/10.1073/pnas.2121730119 Doctor, J. N., Goldstein, N. J., Fox, C. R., Linder, J. A., Persell, S. D., Stewart, E. P., Knight, T. K., & Meeker, D. (2023). Clinician Job Satisfaction After Peer Comparison Feedback: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open, 6(6), e2317379. https://doi.org/10.1001/jamanetworkopen.2023.17379 Ivers, N., Jamtvedt, G., Flottorp, S., Young, J. M., Odgaard-Jensen, J., French, S. D., O'Brien, M. A., Johansen, M., Grimshaw, J., & Oxman, A. D. (2012a). Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev, 2012(6), CD000259. https://doi.org/10.1002/14651858.CD000259.pub3 Rowe, T. A., & Linder, J. A. (2019). Novel approaches to decrease inappropriate ambulatory antibiotic use. Expert Rev Anti Infect Ther, 17(7), 511-521. https://doi.org/10.1080/14787210.2019.1635455 Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487 Footnotes When analyzing using conditional logit regression, a reference term is needed to avoid covariance and rank the regression coefficients (with a reference term of 0) to obtain the respondents' preferences. In our study, the element of "Recipients" was used as the reference term. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx AdditionalFile2.docx Additionalfile3.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-6270141","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443732271,"identity":"9ee7662e-f8db-4c63-b14e-22405b15ac80","order_by":0,"name":"Hamada Kidanga Mussa","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hamada","middleName":"Kidanga","lastName":"Mussa","suffix":""},{"id":443732272,"identity":"56c36046-9c0d-452e-be01-82c818cff0bd","order_by":1,"name":"Xiaoqing Zhu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Zhu","suffix":""},{"id":443732273,"identity":"a11b11d1-e7c8-4a9a-9610-1749cc960b89","order_by":2,"name":"Pramesh Koju","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pramesh","middleName":"","lastName":"Koju","suffix":""},{"id":443732274,"identity":"d51c7636-d65b-4a1f-989c-f02fb4d9d183","order_by":3,"name":"Huadan Huang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huadan","middleName":"","lastName":"Huang","suffix":""},{"id":443732275,"identity":"0e0c83fe-9c0c-4e5d-ba86-1a9bd9642f76","order_by":4,"name":"Huanyuan Luo","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huanyuan","middleName":"","lastName":"Luo","suffix":""},{"id":443732276,"identity":"94f191c8-7403-4ac2-a937-1d6695309d71","order_by":5,"name":"Dong (Roman) Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBADOTYog7GBWC3GpGtJhKkkrMW8vTvxc2Hb4fQ+Bvanm3kYbGQ3HGB+9gCfFpkzZzdLzzhzOLeNgcfsNg9DmvGGA2zmBvi0SEjkbpDmqQBrYQNqOZy44QAPmwQBLZt/8xgcTmdjYH8G1PKfKC3bQLYkAEMM5LADRGjhObvNmudMumEbM4/ZzTkGycYzD7OZ4dfC3rv5Nm+btbx8e/uzG28q7GT7jjc/w6sFAZhBhAGMMQpGwSgYBaOAIgAAd91APY+oEewAAAAASUVORK5CYII=","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"(Roman)","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-03-20 13:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6270141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6270141/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80835482,"identity":"388551bd-ac95-458f-be8b-cb83775ec99e","added_by":"auto","created_at":"2025-04-17 14:45:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7284,"visible":true,"origin":"","legend":"\u003cp\u003eMeans of best-minus-worst scores for seven Feedback components\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6270141/v1/a0186aa7eeea28843673bf47.png"},{"id":81748689,"identity":"92f4d1df-f984-4862-8916-2bfe701ea857","added_by":"auto","created_at":"2025-05-01 05:31:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6270141/v1/8c0cec92-25fe-4420-80a3-f0986f43d9b0.pdf"},{"id":80834131,"identity":"bd68169f-808f-4713-90bd-91c52bd970fd","added_by":"auto","created_at":"2025-04-17 14:37:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25745,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6270141/v1/e71f850940ca17eae4f023b8.docx"},{"id":80835483,"identity":"328cae56-7b40-4ce6-8d3a-b67a95dbf828","added_by":"auto","created_at":"2025-04-17 14:45:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17484,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6270141/v1/6f8cd873ac3777b61e05cf3f.docx"},{"id":80833492,"identity":"715dbe99-5294-4c72-92fc-e8164c82c95d","added_by":"auto","created_at":"2025-04-17 14:29:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24439,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6270141/v1/4a54421f94fa7a1a9e4d4204.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Audit and Feedback in Low-Resource Settings: A Best-Worst Scaling Study of Healthcare Workers Preference in Zanzibar","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdherence to evidence-based clinical guidelines is crucial for providing high-quality Primary Healthcare (PHC) [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Following these guidelines helps ensure more consistent and accurate diagnoses and treatments, thereby minimizing adverse health outcomes. However, Low- and Middle-income Countries (LMICs) like Zanzibar often face challenges in adhering to these guidelines, resulting in suboptimal patient outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAudit and Feedback (A\u0026amp;F) is a widely used strategy to improve professional practice and patient outcomes. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It involves summarizing data about specific aspects of practice and feeding it back to practitioners to encourage improving clinical performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, A\u0026amp;F demonstrated mixed effectiveness in LMICs, mainly due to different practice contexts that require distinct configurations of A\u0026amp;F [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The accuracy of the audit is fundamental to delivering effective feedback. Audits can take many forms, including chart reviews, clinical vignettes, and exit interviews, and in recent years, the Unannounced Standardized Patient (USP) has been progressively used in primary health quality assessment, considering its high reliability and validity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, we still need to identify the optimal feedback configuration to unleash the potential of A\u0026amp;F that meets the low-resource context of LMICs, such as those in Tanzania.\u003c/p\u003e \u003cp\u003eZanzibar, a semi-autonomous region of Tanzania situated about fifteen miles off the coast of Tanzania mainland, exemplifies many challenges of using A\u0026amp;F in LMIC settings. For A\u0026amp;F to enhance engagement and Sustainability, feedback must align with healthcare providers' priorities\u0026mdash;a critical gap in current practice, where top-down designs often neglect local realities. To address this, we applied Best-Worst Scaling (BWS), an innovative participatory method increasingly used in health research for user preferences but underutilized in LMICs compared to high-income countries [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Introduced by Finn and Louviere [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], BWS employs trade-off tasks grounded in Random Utility Theory (RUT) to quantify the relative importance of components within a set [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By asking participants to select the \"best\" and \"worst\" options iteratively, BWS identifies priorities that reflect real-world decision-making [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Hence, this study aimed to identify and rank the feedback components prioritized by Zanzibar's primary healthcare (PHC) providers to determine the most feasible and important feedback strategies for guideline adherence and quality improvement using the Best-Worst Scaling method.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eWe conducted this two-phase, mixed-methods study in PHC facilities across seven districts (North 'A', North' B', West 'A', West 'B', Urban, South, and Central districts) of Zanzibar, Tanzania, from June to December 2024. Phase 1 used a modified Delphi process to identify candidate A\u0026amp;F components, and Phase 2 employed Best-Worst Scaling to quantify healthcare worker preferences. The study adhered to STROBE guidelines for observational studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eZanzibar has a fair network of PHC facilities, which serve a population of about 3000 to 5000 per PHC facility, providing basic healthcare services (for dispensaries) and professional assistance with normal deliveries, laboratory services, and dental care (for Primary Healthcare Centers). PHCs represent the lowest level of the healthcare delivery framework, where prevention, health promotion, and disease management begin.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling method and sample size\u003c/h3\u003e\n\u003cp\u003eWe used purposive sampling, with the research team selecting a list of experts based on their experience and working areas. The same method was used to obtain a representative sample of health workers for the BWS survey.\u003c/p\u003e \u003cp\u003eAcademicians, policymakers, health system administrators, and healthcare providers were invited to the consultation meetings and survey. The exclusion criterion was that the experts have no corresponding expertise or experience in the relevant field.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the survey included clinical officers, nurses, and other healthcare providers dealing with consultation, examination, diagnosis, and disease treatment in public PHC facilities. The exclusion criteria are interns or students who are working during the time of visits. We recruited two research assistants to distribute the questionnaire link.\u003c/p\u003e \u003cp\u003eThe sample size for the expert consultation, using the Delphi method, was determined based on the principle of saturation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As there is no established consensus on the sample size for Delphi studies, we continued recruitment until no new significant insights or themes emerged from the expert meetings and questionnaires.\u003c/p\u003e \u003cp\u003eRegarding the BWS survey, some guidelines exist, but there is no universally accepted standard for determining an adequate sample size [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous research using BWS has employed sample sizes ranging from 15 to 803, with a median of 175 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Considering the sample size used in most studies, we aimed to recruit 80 primary healthcare workers (one from each facility) to complete the formal survey. This sample represented over 50% of Zanzibar's primary healthcare facilities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected using both qualitative and quantitative methods, which involved two main stages. All data for this study were collected and managed through the REDCap system. The Redcap is a free and powerful cloud tool for data collection, storage, and management.\u003c/p\u003e\n\u003ch3\u003eExpert Consultation (Experts meetings and the Delphi method) to identify candidate feedback components\u003c/h3\u003e\n\u003cp\u003e We conducted a comprehensive literature review to identify potential feedback components for guideline adherence in PHC settings. Based on the findings, we refined the components and selected the most important and feasible ones. Fourteen components and their levels (\u003cem\u003eadditional file 1\u003c/em\u003e) were presented to a purposive sample of experts and stakeholders with different backgrounds to avoid bias [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] during three meetings to discuss their feasibility and significance within the local context of Zanzibar. These open meetings allowed experts to reflect and engage in discussions with researchers. We introduced the study using PowerPoint slides outlining potential feedback components, explaining the experts' tasks of evaluating the components' importance and feasibility before selecting the seven most crucial ones. The meetings, facilitated by the principal investigator, lasted approximately 40 to 60 minutes and were recorded and conducted offline. Subsequently, online questionnaires were distributed anonymously to the experts following the Delphi method to create a clear and distinguishable list of feedback intervention components [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Delphi method is a structured approach that facilitates group communication, enabling experts to address complex problems more effectively [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] with the assumption that group consensus yields more meaningful results than individual judgments, regardless of individual expertise [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Delphi process requires at least two rounds of consultations with the same experts and a fixed set of questions to reach a consensus [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, we conducted a second round of expert questionnaires, and the results were uniformly entered into REDCap via the distributed online questionnaire link. A summary of the meetings and results of the first round of online surveys was prepared. In an online survey, the experts were asked to select one option given for each of the fourteen components to rank the component importance. The choices for components' importance were \u0026ldquo;Not Important at all, Slightly Important, Moderately Important, Important\u0026rdquo; and \u0026ldquo;Extremely Important\u0026rdquo;. Moreover, the options for the components\u0026rsquo; feasibility were \u0026ldquo;Not Feasible, Slightly Feasible (with many constraints), Moderately Feasible (with some constraints)\u0026rdquo;, and \u0026ldquo;Highly Feasible\u0026rdquo;. After the first round of questionnaires, feedback was given to experts so that they could assess their opinions with the rest of the others, hence reaching a consensus for the second round.\u003c/p\u003e\n\u003ch3\u003eBest-worst scaling (BWS) to rank feedback components\u003c/h3\u003e\n\u003cp\u003eBWS is a survey method to elicit individual preferences [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] that has three common cases (types): the object case, profile case, and Multi-profile cases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We employed the object case to rank feedback components, as this approach is optimal for prioritizing discrete attributes rather than evaluating multi-attribute profiles. BWS requires participants to select the most (best) and least (worst) preferred components in repeated choice tasks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One advantage of using BWS is that it gives us an understanding of the \"best and worst\" choice for respondents, unlike conventional Discrete Choice Experiment (DCE) where respondents can only choose the \"best\" [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter selecting the top seven feedback components from the Delphi process, the BWS survey was employed to rank and identify three potentially most effective and feasible components. The BWS survey used the Balanced Incomplete Block Design (BIBD) with R software version 4.4.2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (\u003cem\u003eadditional file 2\u003c/em\u003e). BIBD organizes items into blocks and balances the number of items presented across participants, which allows for efficient comparisons among a set of items, reducing bias and increasing the statistical precision of the ratings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the seven feedback components were organized into seven different blocks, each containing three A\u0026amp;F components, with each component appearing three times in the questionnaire [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We explained and provided examples for each component so respondents could understand its meaning clearly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We invited participants to reflect on which component of these seven blocks is most effective (most important) and which is least effective (least important). We assigned 1 point when a component was chosen as most effective (best) and \u0026minus;\u0026thinsp;1 when a component was selected as least effective (worst). The results from the BWS survey were then used to finalize the three most effective components. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an example of a BWS block question implemented through REDcap.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAn Example of a BWS Questionnaire Configuration file\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTo improve the quality of care, we plan to organize an intervention to measure and evaluate key health quality indicators for practitioners (completion rate of key consultation and examination items, diagnosis and treatment, etc. specified in medical guidelines) and then provide feedback to practitioners on the evaluation results, i.e., an \"Audit and Feedback\" intervention. Now, you will see some multiple-choice questions containing three items for the \"Feedback\" component in blocks. Which feedback components (factors) do you consider the MOST IMPORTANT in promoting guidelines adherence in managing diseases? And which one is LEAST IMPORTANT?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost important (Best)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFeedback Component\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eExplanation with examples\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLeast important (Worst)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeedback with Peer Comparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe feedback report will include feedback on the health worker's clinical performance and comparisons with peers and other health facilities.\u003c/p\u003e \u003cp\u003eFor example, your current adherence rate to managing diabetes as per the NCD protocol is 60%, whereas Kidoti dispensary's is 75%, and Matemwe dispensary's is 80%.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeedback with Goals/Targets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn addition to feedback on the health worker's clinical performance, the feedback report will include the goals/targets to be achieved regarding clinical performance for the management of hypertension and diabetes within a certain period.\u003c/p\u003e \u003cp\u003eFor example, your current adherence rate to standard diabetes management as per the NCD protocol is 60%. You have to increase it to 80% in the next 3 months.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormat of the Feedback Report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe format of the Feedback Report means that the feedback is organized in a certain specific way. This can be;\u003c/p\u003e \u003cp\u003e\"Textual report\", in which the feedback report is in text format only. For example, the feedback would be a line like \"Your hypertension diagnosis counseling completion rate is 60%\".\u003c/p\u003e \u003cp\u003e\"Feedback report designed with texts and visual displays\". This means that the feedback report is in text and visual display formats. For example, bar graphs and charts show how well healthcare professionals are implementing hypertension and diabetes guidelines in the clinical setting.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter developing the BWS questionnaires, we conducted a pilot survey involving ten participants. Verbal informed consent was obtained before the pilot survey. After completing the BWS questionnaires (average time: 15 minutes), participants were invited to provide feedback on the questionnaires. Based on their suggestions, we refined the questionnaires for the formal survey.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis for the expert consultations was performed using Microsoft Excel/IBM SPSS 25.0. Approaches were used to analyze expert consultation data including the expert Authority (Cr) for the Delphi method, Threshold Method, and Kendall's Concordance coefficient.\u003c/p\u003e \u003cp\u003eExpert Authority (Cr) for the Delphi method: The expert authority coefficient is an important factor used to judge the validity of consultation results. The expert authority coefficient (Cr) was determined by two (2) factors: the expert's judgment basis on the consultation items (Ca) and the expert's degree of familiarity with the items (Cs). (Cr)=(Ca\u0026thinsp;+\u0026thinsp;Cs)/2. High Cr values (\u0026gt;\u0026thinsp;0.7) indicate a high degree of expert authority [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThreshold Method: This involves setting a threshold based on the mean scores for importance, the rate of full marks, and the degree of variation. Indicators that fall below or exceed this threshold are removed [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKendall's coefficient: The number of consultation rounds can be decided based on Kendall's coefficient, which requires stopping when Kendall's coefficient exceeds 0.7. It was used to measure the agreement of the experts\u0026rsquo; opinions.\u003c/p\u003e \u003cp\u003eThe mean score for each item's importance and the coefficient of variation (CV) evaluated the experts\u0026rsquo; opinions.\u003c/p\u003e \u003cp\u003eR Software, version 4.4.2, was used to analyze the BWS data through two approaches; counting and modeling. First, we conducted a count analysis to assess the frequency of each feedback component's selection. The Best-Worst Scaling (BW) score was calculated by subtracting the number of times a component was selected as the 'least' important from the number of times it was chosen as the 'most' important. Next, we applied the conditional logit model in our modeling approach. The best-worst mean score determined each component's importance, ranked from largest to smallest. A score near +\u0026thinsp;1.0 indicates greater importance, while a score near \u0026minus;\u0026thinsp;1.0 suggests lesser importance. We calculated the frequency of each component selected as best or worst, along with a 95% confidence interval, enabling us to identify the three most important components.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a part of an Audit and Feedback study in Zanzibar, Tanzania approved by The Zanzibar Health Research Institute (ZAHRI) through its Zanzibar Health Research Ethical Committee (ZAHREC) with ref: No. ZAHREC/05/ST/AUG/2023/149), with Government approval (Permit No. Ref: 2001710219233839296653).\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eParticipant Demographics for expert meetings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNine experts participated in the Delphi process (\u003cem\u003eTable 2\u003c/em\u003e). The majority (77.8%) were male, with a mean age of 44.11 years. Most experts (77.8%) held a master\u0026apos;s degree, and the mean professional experience was 11.78 years (SD = 8.09).\u003c/p\u003e\n\u003cp\u003eTable 2 Expert Meeting participant demographics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eItem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; Standard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e77.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 204px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e44.11 \u0026plusmn; 9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eDiploma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eBachelor degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e77.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 204px;\"\u003e\n \u003cp\u003eYears of professional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e11.78 \u0026plusmn; 8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eWork title\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAcademicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eResearchers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eAdministrators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eMedical doctor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDelphi analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpert enthusiasm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effective recovery rate of expert consultation questionnaires from both rounds of expert consultation was 100.0%, and the proportion of expert suggestions was 100.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpert Authority (Cr)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expert authority coefficient (Cr) was 0.848 for the first round and 0.967 for the second round, indicating a high degree of expert authority. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComponents selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc185632453\"\u003eAfter the first round, nine components met the threshold requirements to be selected (SD \u003cem\u003e=\u003c/em\u003e 0.706). The Kendall\u0026apos;s coefficient for the first round was 0.443 (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Following the second round, Kendall\u0026apos;s coefficient increased to 0.803 (p \u0026lt; 0.001). After two rounds of expert consultation and based on the research interest of the research team seven final components and their levels were selected for the next step of the BWS questionnaire (Feedback with clinical guidelines was deleted and Feedback with Peer comparison was chosen because of the interest of the research team). See \u003cem\u003etable 3\u003c/em\u003e below;\u003c/p\u003e\n\u003cp\u003eTable 3 Components selected for the BWS questionnaire\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eComponent\u0026apos;s name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eComponent\u0026apos;s levels\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eFeedback with an improvement plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eYes\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNo\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eFeedback with goals/targets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eYes\u003c/li\u003e\n \u003cli\u003eNo\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eSource of Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eAuthoritative Body\u003c/li\u003e\n \u003cli\u003eResearcher\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eDelivery Method of Feedback Report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eFace to Face\u003c/li\u003e\n \u003cli\u003ePhone calls\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eFeedback Recipients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eAuthoritative body\u003c/li\u003e\n \u003cli\u003eHealth worker\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e6.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eFormat of the Feedback Report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eText only\u003c/li\u003e\n \u003cli\u003eText with graphs\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e7.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 352px;\"\u003e\n \u003cp\u003eFeedback with Peer comparison\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003col\u003e\n \u003cli\u003eYes\u003c/li\u003e\n \u003cli\u003eNo\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBest-worst scaling analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant Demographics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 80 targeted healthcare facilities, 77 (96%) responded to the survey. Four responses were excluded for quality issues, and three facilities (3.75%) were unavailable due to construction work. In the end, 73 (91.25%) provided usable data (\u003cem\u003eTable 4\u003c/em\u003e). The respondents are gender-balanced, mostly diploma holders (89.04%) working as clinical officers (71.23%) in dispensaries (66%), averaging 7.5 years of healthcare experience.\u003c/p\u003e\n\u003cp\u003eTable 4 Background information of participants for the BWS survey\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eItem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; Standard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e46.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e53.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 204px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e33.23 \u0026plusmn; 7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eDiploma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e89.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eAdvanced Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eBachelor degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 204px;\"\u003e\n \u003cp\u003eYears of experience in healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e7.55 \u0026plusmn; 7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eProfessional field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e78.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eNursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eHealth technology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eDesignation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMedical Doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eClinical officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e71.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e21.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 204px;\"\u003e\n \u003cp\u003eYears of Experience in Managing Hypertension and Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eType of Health Facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePrimary health Care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e34.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\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: 126px;\"\u003e\n \u003cp\u003eDispensary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e65.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCounting approach for BWS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe three most important components identified were \u0026quot;Feedback with Improvement Plan\u0026quot;, \u0026quot;Feedback with Goals/targets\u0026quot;, and the \u0026quot;Delivery method of feedback\u0026quot; (table 5 and Figure 1). The error bars indicate a 95% confidence interval.\u003c/p\u003e\n\u003cp id=\"_Toc185632459\"\u003eTable 5 Feedback components scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003emeanB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003emeanW \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003emeanBW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003emean.stdBW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003esqrtBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.8082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.5205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.7123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.2374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.7291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGoals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.7123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.5068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.5023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.8868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePlan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.7671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.5890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.8577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDelivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.1918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.7534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.4384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.1461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.2577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eFormat\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.4658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.1918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.7260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.2420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.6251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.4384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.4658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.0274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.3425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.5469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRecipients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.3699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.6164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.2466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.4155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.4783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFrom \u003cem\u003efigure 1\u003c/em\u003e, a score close to +1.0 indicates that the element is relatively more important, while a score close to -1.0 suggests the opposite.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeling approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe modeling approach yielded the same top three components (\u003cem\u003eTable 6\u003c/em\u003e). \u0026quot;Feedback with Peer Comparison\u0026quot;, \u0026quot;Format of the Feedback Report,\u0026quot; and \u0026quot;Source of Feedback\u0026quot; had negative mean scores (p = 0.0200, p = 0.0217, and p = 0.3408, respectively). \u0026quot;Feedback Recipient\u0026quot; had the smallest mean score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Results from the Modeling Approach\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eExponential coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eStandard error\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePlan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.6541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5.2284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGoals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.4791 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.3888 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1382\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.703 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.1173 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.1245 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1232 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.953 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.3408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDelivery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.8758 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2.4008 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1258 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6.962\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eComparison\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.2846 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.3292 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1224 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.326 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.0200*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eFormat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.2810 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.3245 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1224 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.296 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.0217*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRecipients\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003csup\u003e[1]\u003c/sup\u003e\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eNA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNA \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0000 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNA \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Indicates statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreference heterogeneous\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale participants prioritized feedback delivery methods more highly than females, while other preferences remained similar between genders (\u003cem\u003eadditional file 3\u003c/em\u003e).\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eThis study aimed to identify the feedback components that healthcare workers prioritize to enhance guideline adherence and quality of care in Zanzibar\u0026apos;s primary healthcare settings. Our findings highlight that \u0026quot;Feedback with an Improvement Plan,\u0026quot; \u0026quot;Feedback with Goals/Targets,\u0026quot; and \u0026quot;Delivery Method of Feedback\u0026quot; are the most valued components by PHC workers. While audit and feedback is a widely used and important strategy to improve adherence to best practices, few studies have explored primary healthcare workers\u0026apos; preferences for feedback modalities in LMICs. Thus, our study provides valuable insights for policymakers and healthcare organizations, emphasizing the need to tailor feedback mechanisms to the local context for quality improvement efforts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese preferences align with the understanding that actionable feedback linked to concrete action plans [7], \u0026nbsp;and specific goals [37,][38] is most effective in driving behavior change and professional development [39]. This might indicate that health workers need clear actionable and goal-oriented feedback that address the areas for improvement. This will more likely leads to behavior change among health worker hence improving the quality of health services they deliver. In low resource settings like Zanzibar, this can give a roadmap to overcome some challenges in healthcare delivery. The goal oriented feedbacks can help to prioritize tasks and therefore improve efficiency in resource utilization. Additionally, the importance of delivery method echoes previous findings that feedback from a supervisor or respected colleague can be particularly impactful [40,][41]. This highlights the need to understand the need for feedback to be communicated in a way that will encourage health workers to act in a constructive manner. In low resource settings like Zanzibar where interpersonal relationship varies, it is important to consider how feedback is delivered to enhance the overall quality of the services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile our findings resonate with existing literature on feedback effectiveness, some discrepancies were also observed. Notably, the \u0026quot;Source of Feedback\u0026quot; was not prioritized by PHC workers in our study, contrasting with previous research suggesting that feedback from a trusted source is more impactful [7]. This difference might be attributed to the specific context of Zanzibar\u0026apos;s PHC system where authority figures may not always be perceived as supportive or accessible. Alternatively, the characteristics of our study sample, which consisted predominantly of clinical officers and nurses with diploma-level education, might have influenced these preferences. Many previous studies focused on stakeholders in general and not health workers alone. Likewise, the source was mentioned in our study as an important component in the experts consultation but not in the BWS survey with primary health workers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study found that \u0026quot;Feedback with peer comparison\u0026quot; was not a feedback preference for guideline adherence among PHC workers in Zanzibar. This is similar to some previous studies [42], which suggested that \u0026quot;peer comparison in primary care\u0026quot; was ineffective and reduced job satisfaction. Health workers do not like the to be compared with others. They need to feel the sense of independence in their daily activities. Therefore, feedback should focus on individual growth and development rather than comparison to others. However, this contradicts another recent study, a secondary analysis of a randomized clinical trial [43]. Further research is needed to explore these contextual variations and their implications for feedback design.\u003c/p\u003e\n\u003cp\u003eInterestingly, our results diverge from findings in high-income countries, where peer comparison and data-driven feedback are often emphasized [44,][45]. \u0026nbsp; This underscores the importance of considering contextual factors when designing feedback interventions in LMICs. Resource constraints, cultural norms, and existing healthcare infrastructure can all influence the feasibility and effectiveness of different feedback modalities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults from this study show how males and females perceive different feedback components as influencers to guidelines adherence. For example, the \u0026ldquo;Delivery method of feedback\u0026rdquo; shows higher positive scores for males than females. This may indicate that men have higher priorities in certain communication styles which can influence their behavior than women. The positive scores indicate that feedback components align with the preferences of both genders [46].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key strength of this study lies in its innovative application of Best-Worst Scaling (BWS) to quantify preferences within a resource-constrained setting. BWS distinguishes itself from conventional surveys by forcing clear best-worst choices rather than neutral responses common in the Likert scale, reducing social desirability bias (tendency to agree), providing better item differentiation, and mirroring real-world decisions. To our knowledge, this is the first BWS study to prioritize A\u0026amp;F components in sub-Saharan Africa, addressing the need for participatory methods in LMIC health systems research. Additionally, the mixed-methods design, incorporating qualitative stakeholder consultation for consensus-building alongside the BWS survey, further strengthen the study\u0026apos;s validity and applicability to local contexts.\u003c/p\u003e\n\u003cp\u003eHowever, we also acknowledge several limitations. First, the prioritization of attributes was limited to those selected through the Delphi process; other factors influencing guideline adherence may exist and warrant consideration in future research. Second, while BWS effectively simulates real-world trade-offs, actual behavior may not always align with stated preferences. Future research, which we have planned, will examine the effectiveness of these prioritized components in real-world A\u0026amp;F interventions. Finally, contextual differences may limit the direct generalizability of our findings to other LMIC settings. Nonetheless, the insights from this study in a sub-Saharan African context may be more relevant to similar settings than findings from developed countries. Despite these limitations, our study provides insights for optimizing feedback strategies in resource-constrained settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealthcare organizations and policymakers in Zanzibar and similar contexts should prioritize feedback mechanisms that include clear improvement plans and goal setting, while also considering the most appropriate delivery methods for their workforce. Further research is needed to evaluate the effectiveness of these strategies in improving guideline adherence and patient outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing Best-Worst Scaling as part of the audit and feedback strategy has promising results for improving guideline adherence in primary healthcare settings within low- and middle-income countries like Zanzibar. The findings from this study have found that the most preferred feedback components among health workers are \u0026quot;Feedback with an Improvement Plan,\u0026quot; \u0026quot;Feedback with Goals/Targets,\u0026quot; and \u0026quot;Delivery Method of Feedback\u0026quot;. The sample size is enough for the results to be generalized in Zanzibar and similar settings. Healthcare organizations (the Ministry of Health) should establish structured feedback processes incorporating improvement plans and goal-setting as central elements. Further research should focus on testing these feedback components in real work environments using audit and feedback interventions. Similarly, future studies may need to increase the number of feedback components to be evaluated by health workers. Moreover, we recommend a qualitative study to get a deeper knowledge of facilitators and barriers to the feedback components.\u0026nbsp;\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eA\u0026amp;F\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Audit and feedback\u003c/p\u003e\n\u003cp\u003eBIBD Balanced Incomplete Block Design\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBWS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Best worst Scaling\u003c/p\u003e\n\u003cp\u003eDCE Discrete Choice Experiment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEBP Evidence-based practice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMICs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low- and middle-income countries\u003c/p\u003e\n\u003cp\u003ePI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Principal Investigator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Primary Healthcare\u003c/p\u003e\n\u003cp\u003eREDCap\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Research Electronic Data Capture\u003c/p\u003e\n\u003cp\u003eRUT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Random Utility Theory\u003c/p\u003e\n\u003cp\u003eSDGs Sustainable Development Goals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSTROBE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;STrengthening the Reporting of OBservational studies in Epidemiology.\u003c/p\u003e\n\u003cp\u003eUHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Universal Health Coverage\u003c/p\u003e\n\u003cp\u003eUSP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unannounced Standardized Patient\u003c/p\u003e\n\u003cp\u003eZAHREC Zanzibar Health Research Ethical Committee\u003c/p\u003e\n\u003cp\u003eZAHRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Zanzibar Health Research Institute\u0026nbsp;\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 is a part of an Audit and Feedback study in Zanzibar which was\u0026nbsp;reviewed\u0026nbsp;and approved by\u0026nbsp;The Zanzibar Health Research Institute (ZAHRI) through its Zanzibar Health Research Ethical Committee (ZAHREC) with Ref: NO. ZAHREC/05/ST/AUG/2023/149 and got Government approval with permit no. Ref: 2001710219233839296653. Before the study, we sent electronic/paper-based materials, including an informed consent form, to the participants. The study did not start until the participants\u0026apos; informed consent forms were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (72404118), China Postdoctoral Science Foundation (2023M731536), Guangdong Medical Science and Technology Research Foundation (A2023133), and Swiss Agency for Development and Cooperation\u0026nbsp;(81067392).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization\u0026mdash;DX; Methodology \u0026ndash; HK, XZ \u0026amp; HL; Data Curation: XZ \u0026amp; HK; Formal analysis \u0026ndash; HK \u0026amp; XZ; Writing of Original draft \u0026ndash; HK; Writing review \u0026amp; editing \u0026ndash; HK, HH, PK \u0026amp; XZ; Supervision \u0026amp; Validation \u0026ndash;DX \u0026amp; HL\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the funding from the National Natural Science Foundation of China (72404118), China Postdoctoral Science Foundation (2023M731536), Guangdong Medical Science and Technology Research Foundation (A2023133), and Swiss Agency for Development and Cooperation\u0026nbsp;(81067392).\u003c/p\u003e\n\u003cp\u003eWe also acknowledge the Revolutionary Government of Zanzibar for approving the research project. Our special thanks to all healthcare workers whose active participation was crucial to the progress of my project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. School of Health Management, Southern Medical University, Guangzhou, China\u003c/p\u003e\n\u003cp\u003e2. School of Health and Medical Sciences, the State University of Zanzibar\u003c/p\u003e\n\u003cp\u003e3. School of Public Health, Southern Medical University, Guangzhou, China\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp;Pharmacovigilance Unit / Department of Public Health and Community Programs, Dhulikhel Hospital, Kathmandu University Hospital, Dhulikhel, Nepal\u003c/p\u003e\n\u003cp\u003e5. SMU Institute for Global Health\u0026nbsp;(SIGHT) and Center for World Health Organization Studies,\u0026nbsp;School of Health Management and Dermatology Hospital of Southern Medical University (SMU), Guangzhou, China\u003c/p\u003e\n\u003cp\u003e6. Acacia Lab for Implementation Science, School of Health Management and Dermatology Hospital, Southern Medical University, Guangzhou, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e# Contributed equally to this work\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cstrong\u003eCorresponding authors:\u003c/strong\u003e 1) Prof. Dong (Roman) Xu. Email: [email protected] 2) Huanyuan Luo, Email. [email protected]\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWiedenmayer, K., Ombaka, E., Kabudi, B., Canavan, R., Rajkumar, S., Chilunda, F., Sungi, S., \u0026amp; Stoermer, M. (2021). Adherence to standard treatment guidelines among prescribers in primary healthcare facilities in the Dodoma region of Tanzania. BMC Health Serv Res, 21(1), 272. https://doi.org/10.1186/s12913-021-06257-y \u003c/li\u003e\n\u003cli\u003eAdams, O. P., \u0026amp; Carter, A. O. (2010). Diabetes and hypertension guidelines and the primary health care practitioner in Barbados: knowledge, attitudes, practices and barriers--a focus group study. BMC Fam Pract, 11, 96. https://doi.org/10.1186/1471-2296-11-96 \u003c/li\u003e\n\u003cli\u003eLiu, X. L., Wang, T., Tan, J. Y., Stewart, S., Chan, R. J., Eliseeva, S., Polotan, M. J., \u0026amp; Zhao, I. (2022). Sustainability of healthcare professionals\u0026apos; adherence to clinical practice guidelines in primary care. BMC Prim Care, 23(1), 36. https://doi.org/10.1186/s12875-022-01641-x \u003c/li\u003e\n\u003cli\u003eIvers, N., Jamtvedt, G., Flottorp, S., Young, J. M., Odgaard-Jensen, J., French, S. D., O\u0026apos;Brien, M. A., Johansen, M., Grimshaw, J., \u0026amp; Oxman, A. D. (2012b). Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev(6), CD000259. https://doi.org/10.1002/14651858.CD000259.pub3 \u003c/li\u003e\n\u003cli\u003eHysong, S. J., Best, R. G., \u0026amp; Pugh, J. A. (2006). Audit and feedback and clinical practice guideline adherence: making feedback actionable. Implement Sci, 1, 9. https://doi.org/10.1186/1748-5908-1-9 \u003c/li\u003e\n\u003cli\u003eBrown, B., Gude, W. T., Blakeman, T., van der Veer, S. N., Ivers, N., Francis, J. J., Lorencatto, F., Presseau, J., Peek, N., \u0026amp; Daker-White, G. (2019). Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci, 14(1), 40. https://doi.org/10.1186/s13012-019-0883-5 \u003c/li\u003e\n\u003cli\u003eColquhoun, H. L., Carroll, K., Eva, K. W., Grimshaw, J. M., Ivers, N., Michie, S., \u0026amp; Brehaut, J. C. (2021). Informing the research agenda for optimizing audit and feedback interventions: results of a prioritization exercise. BMC Med Res Methodol, 21(1), 20. https://doi.org/10.1186/s12874-020-01195-5 \u003c/li\u003e\n\u003cli\u003eMeherali, S., Punjani, N. S., \u0026amp; Mevawala, A. (2020). Health Literacy Interventions to Improve Health Outcomes in Low- and Middle-Income Countries. Health Lit Res Pract, 4(4), e251-e266. https://doi.org/10.3928/24748307-20201118-01 \u003c/li\u003e\n\u003cli\u003eXu, D. R., Cai, Y., Wang, X., Chen, Y., Gong, W., Liao, J., Zhou, J., Zhou, Z., Zhang, N., Tang, C., Mi, B., Lu, Y., Wang, R., Zhao, Q., He, W., Liang, H., Li, J., \u0026amp; Pan, J. (2022). Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients. Am J Public Health, 112(6), 913-922. https://doi.org/10.2105/ajph.2022.306779 \u003c/li\u003e\n\u003cli\u003eSchuster, A. L. R., Crossnohere, N. L., Campoamor, N. B., Hollin, I. L., \u0026amp; Bridges, J. F. P. (2024). The rise of best-worst scaling for prioritization: A transdisciplinary literature review. Journal of Choice Modelling, 50. https://doi.org/10.1016/j.jocm.2023.100466 \u003c/li\u003e\n\u003cli\u003eLandis-Lewis, Z., Kononowech, J., Scott, W. J., Hogikyan, R. V., Carpenter, J. G., Periyakoil, V. S., Miller, S. C., Levy, C., Ersek, M., \u0026amp; Sales, A. (2020). Designing clinical practice feedback reports: three steps illustrated in Veterans Health Affairs long-term care facilities and programs. Implement Sci, 15(1), 7. https://doi.org/10.1186/s13012-019-0950-y \u003c/li\u003e\n\u003cli\u003eWright-Hughes, A., Willis, T. A., Wilson, S., Weller, A., Lorencatto, F., Althaf, M., Seymour, V., Farrin, A. J., Francis, J., Brehaut, J., Ivers, N., Alderson, S. L., Brown, B. C., Feltbower, R. G., Gale, C. P., Stanworth, S. J., Hartley, S., Colquhoun, H., Presseau, J.,\u0026hellip;Foy, R. (2022). A randomised fractional factorial screening experiment to predict effective features of audit and feedback. Implement Sci, 17(1), 34. https://doi.org/10.1186/s13012-022-01208-5 \u003c/li\u003e\n\u003cli\u003eQuigley, M., Earnest, A., Andrikopoulos, S., Wischer, N., Green, S., \u0026amp; Zoungas, S. (2022). ANDA-Evaluating Facilitated Feedback Enhancement - a Cluster randomised Trial (ANDA-EFFECT): protocol for a cluster randomised trial of audit feedback augmented with education and support, compared to feedback alone, on acceptability, utility and health outcomes in diabetes centres in Australia. Trials, 23(1), 976. https://doi.org/10.1186/s13063-022-06910-9 \u003c/li\u003e\n\u003cli\u003eShoji, Y., Kim, H., Kubo, T., Tsuge, T., Aikoh, T., \u0026amp; Kuriyama, K. (2021). Understanding preferences for pricing policies in Japan\u0026rsquo;s national parks using the best\u0026ndash;worst scaling method. Journal for Nature Conservation, 60. https://doi.org/10.1016/j.jnc.2021.125954 \u003c/li\u003e\n\u003cli\u003eFlynn, T. N., Louviere, J. J., Peters, T. J., \u0026amp; Coast, J. (2007). Best--worst scaling: What it can do for health care research and how to do it. J Health Econ, 26(1), 171-189. https://doi.org/10.1016/j.jhealeco.2006.04.002 \u003c/li\u003e\n\u003cli\u003eHall, R., Medina-Lara, A., Hamilton, W., \u0026amp; Spencer, A. (2022). Women\u0026apos;s priorities towards ovarian cancer testing: a best-worst scaling study. BMJ Open, 12(9), e061625. https://doi.org/10.1136/bmjopen-2022-061625 \u003c/li\u003e\n\u003cli\u003ePotoglou, D., Burge, P., Flynn, T., Netten, A., Malley, J., Forder, J., \u0026amp; Brazier, J. E. (2011). Best-worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med, 72(10), 1717-1727. https://doi.org/10.1016/j.socscimed.2011.03.027 \u003c/li\u003e\n\u003cli\u003eWittenberg, E., Bharel, M., Bridges, J. F., Ward, Z., \u0026amp; Weinreb, L. (2016). Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women. Ann Fam Med, 14(4), 359-364. https://doi.org/10.1370/afm.1937 \u003c/li\u003e\n\u003cli\u003evon Elm, E., Altman, D. G., Egger, M., Pocock, S. J., G\u0026oslash;tzsche, P. C., \u0026amp; Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet, 370(9596), 1453-1457. https://doi.org/10.1016/s0140-6736(07)61602-x \u003c/li\u003e\n\u003cli\u003eHennink, M., \u0026amp; Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science \u0026amp; medicine, 292, 114523. \u003c/li\u003e\n\u003cli\u003eLouviere J, F. T., Marley A. (2015). Best-Worst Scaling: Theory, Methods and Applications. Cambridge, United Kingdom: Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eXu, R. H., Zhou, L., Wong, E. L., Wang, D., Xiang, G. C., \u0026amp; Xu, C. (2020). A best-worst scaling survey of medical students\u0026apos; perspective on implementing shared decision-making in China. BMC Med Educ, 20(1), 486. https://doi.org/10.1186/s12909-020-02406-9 \u003c/li\u003e\n\u003cli\u003eCheung, K. L., Wijnen, B. F., Hollin, I. L., Janssen, E. M., Bridges, J. F., Evers, S. M., \u0026amp; Hiligsmann, M. (2016). Using Best-Worst Scaling to Investigate Preferences in Health Care. Pharmacoeconomics, 34(12), 1195-1209. https://doi.org/10.1007/s40273-016-0429-5 \u003c/li\u003e\n\u003cli\u003eSpencer, M., Greville‐Heygate, F., Singer, R., Rees, J., \u0026amp; MacDonagh, R. (2015). Health Improvement Project Zanzibar: a new model of healthcare delivery. Trends in Urology \u0026amp; Men\u0026apos;s Health, 6(5), 35-39. https://doi.org/10.1002/tre.485 \u003c/li\u003e\n\u003cli\u003eGarnett, C., Crane, D., West, R., Brown, J., \u0026amp; Michie, S. (2015). Identification of Behavior Change Techniques and Engagement Strategies to Design a Smartphone App to Reduce Alcohol Consumption Using a Formal Consensus Method. JMIR Mhealth Uhealth, 3(2), e73. https://doi.org/10.2196/mhealth.3895 \u003c/li\u003e\n\u003cli\u003eGattrell, W. T., Logullo, P., van Zuuren, E. J., Price, A., Hughes, E. L., Blazey, P., Winchester, C. C., Tovey, D., Goldman, K., Hungin, A. P., \u0026amp; Harrison, N. (2024). ACCORD (ACcurate COnsensus Reporting Document): A reporting guideline for consensus methods in biomedicine developed via a modified Delphi. PLoS Med, 21(1), e1004326. https://doi.org/10.1371/journal.pmed.1004326 \u003c/li\u003e\n\u003cli\u003eNasa, P., Jain, R., \u0026amp; Juneja, D. (2021). Delphi methodology in healthcare research: How to decide its appropriateness. World J Methodol, 11(4), 116-129. https://doi.org/10.5662/wjm.v11.i4.116 \u003c/li\u003e\n\u003cli\u003eNiederberger, M., \u0026amp; Spranger, J. (2020). Delphi Technique in Health Sciences: A Map. Front Public Health, 8, 457. https://doi.org/10.3389/fpubh.2020.00457 \u003c/li\u003e\n\u003cli\u003eZhai, X. D., Wang, C. X., Ma, Y. J., Yu, J. X., Xiang, S. S., Jiao, H. Y., Shao, P., Guan, X., Wang, J., \u0026amp; Zhang, H. Q. (2020). Using the modified Delphi method to research the influencing factors of long-term health-related quality of life in patients with unruptured intracranial aneurysms after endovascular treatment. Chin Neurosurg J, 6, 7. https://doi.org/10.1186/s41016-020-00186-1 \u003c/li\u003e\n\u003cli\u003eLuo, N., Uy, E. J. B., Bautista, D. C., Xin, X., Cheung, Y. B., Thio, S.-T., \u0026amp; Thumboo, J. (2018). Using best-worst scaling choice experiments to elicit the most important domains of health for health-related quality of life in Singapore. Plos One, 13(2). https://doi.org/10.1371/journal.pone.0189687 \u003c/li\u003e\n\u003cli\u003eMuhlbacher, A. C., Kaczynski, A., Zweifel, P., \u0026amp; Johnson, F. R. (2016). Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview. Health Econ Rev, 6(1), 2. https://doi.org/10.1186/s13561-015-0079-x \u003c/li\u003e\n\u003cli\u003eAizaki, H., \u0026amp; Fogarty, J. (2023). R packages and tutorial for case 1 best\u0026ndash;worst scaling. Journal of Choice Modelling, 46. https://doi.org/10.1016/j.jocm.2022.100394 \u003c/li\u003e\n\u003cli\u003eM\u0026uuml;hlbacher, A. C., Zweifel, P., Kaczynski, A., \u0026amp; Johnson, F. R. (2016). Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues. Health Econ Rev, 6(1), 5. https://doi.org/10.1186/s13561-015-0077-z \u003c/li\u003e\n\u003cli\u003eHollin, I. L., Paskett, J., Schuster, A. L. R., Crossnohere, N. L., \u0026amp; Bridges, J. F. P. (2022). Best-Worst Scaling and the Prioritization of Objects in Health: A Systematic Review. Pharmacoeconomics, 40(9), 883-899. https://doi.org/10.1007/s40273-022-01167-1 \u003c/li\u003e\n\u003cli\u003eZhao, Y., He, L., Hu, J., Zhao, J., Li, M., Huang, L., Jin, Q., Wang, L., \u0026amp; Wang, J. (2022). Using the Delphi method to establish pediatric emergency triage criteria in a grade A tertiary women\u0026apos;s and children\u0026apos;s hospital in China. BMC Health Serv Res, 22(1), 1154. https://doi.org/10.1186/s12913-022-08528-8 \u003c/li\u003e\n\u003cli\u003eShi, C., Zhang, Y., Li, C., Li, P., \u0026amp; Zhu, H. (2020). Using the Delphi Method to Identify Risk Factors Contributing to Adverse Events in Residential Aged Care Facilities. Risk Manag Healthc Policy, 13, 523-537. https://doi.org/10.2147/rmhp.S243929 \u003c/li\u003e\n\u003cli\u003eLocke, E. A., \u0026amp; Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. Am Psychol, 57(9), 705-717. https://doi.org/10.1037//0003-066x.57.9.705 \u003c/li\u003e\n\u003cli\u003eLloyd, R., Munro, J., Evans, K., Gaskin-Williams, A., Hui, A., Pearson, M., Slade, M., Kotera, Y., Day, G., Loughlin-Ridley, J., Enston, C., \u0026amp; Rennick-Egglestone, S. (2023). Health service improvement using positive patient feedback: Systematic scoping review. Plos One, 18(10), e0275045. https://doi.org/10.1371/journal.pone.0275045 \u003c/li\u003e\n\u003cli\u003eRapin, J., Gendron, S., Mabire, C., \u0026amp; Dubois, C. A. (2023). Feedback on clinical team performance: how does it work, in what contexts, for whom, and for what changes? A critical realist qualitative multiple case study. BMC Health Serv Res, 23(1), 410. https://doi.org/10.1186/s12913-023-09402-x \u003c/li\u003e\n\u003cli\u003eIvers, N. M., Grimshaw, J. M., Jamtvedt, G., Flottorp, S., O\u0026apos;Brien, M. A., French, S. D., Young, J., \u0026amp; Odgaard-Jensen, J. (2014). Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med, 29(11), 1534-1541. https://doi.org/10.1007/s11606-014-2913-y \u003c/li\u003e\n\u003cli\u003eHattie, J. A. C., \u0026amp; Timperley, H. S. (2007). The Power of Feedback. Review of Educational Research, 77, 112 - 181. \u003c/li\u003e\n\u003cli\u003eReiff, J. S., Zhang, J. C., Gallus, J., Dai, H., Pedley, N. M., Vangala, S., Leuchter, R. K., Goshgarian, G., Fox, C. R., Han, M., \u0026amp; Croymans, D. M. (2022). When peer comparison information harms physician well-being. Proc Natl Acad Sci U S A, 119(29), e2121730119. https://doi.org/10.1073/pnas.2121730119 \u003c/li\u003e\n\u003cli\u003eDoctor, J. N., Goldstein, N. J., Fox, C. R., Linder, J. A., Persell, S. D., Stewart, E. P., Knight, T. K., \u0026amp; Meeker, D. (2023). Clinician Job Satisfaction After Peer Comparison Feedback: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open, 6(6), e2317379. https://doi.org/10.1001/jamanetworkopen.2023.17379 \u003c/li\u003e\n\u003cli\u003eIvers, N., Jamtvedt, G., Flottorp, S., Young, J. M., Odgaard-Jensen, J., French, S. D., O\u0026apos;Brien, M. A., Johansen, M., Grimshaw, J., \u0026amp; Oxman, A. D. (2012a). Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev, 2012(6), CD000259. https://doi.org/10.1002/14651858.CD000259.pub3 \u003c/li\u003e\n\u003cli\u003eRowe, T. A., \u0026amp; Linder, J. A. (2019). Novel approaches to decrease inappropriate ambulatory antibiotic use. Expert Rev Anti Infect Ther, 17(7), 511-521. https://doi.org/10.1080/14787210.2019.1635455 \u003c/li\u003e\n\u003cli\u003eHattie, J., \u0026amp; Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e When analyzing using conditional logit regression, a reference term is needed to avoid covariance and rank the regression coefficients (with a reference term of 0) to obtain the respondents' preferences. In our study, the element of \"Recipients\" was used as the reference term.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Audit and Feedback, Best-worst Scaling, Quality, Primary Healthcare, Evidence-based practice","lastPublishedDoi":"10.21203/rs.3.rs-6270141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6270141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e Adherence to clinical guidelines is crucial for providing high-quality primary healthcare services. However, low- and middle-income countries (LMICs) like Zanzibar often face challenges adhering to the guidelines. Audit and feedback (A\u0026amp;F) mechanisms can play a significant role in identifying areas for improvement and promoting guideline compliance. Traditional feedback methods may not effectively prioritize the most impactful changes. Best-worst scaling (BWS) can provide a more nuanced approach to understanding preferences and priorities among healthcare practitioners. This study aims at optimizing feedback strategy by determining which components health workers prioritize.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis mixed-method study was done under two methodological approaches a) expert consultation and b) Best-worst scaling. Expert consultation was conducted to seek expert opinions on the various feedback components obtained from the literature. This was done through expert meetings and questionnaires. Three meetings were conducted and two rounds of questionnaires were distributed. A BWS survey was done to find the prioritized components from the primary healthcare workers. A series of questions was presented to them arranged in blocks to select the most important (best) and the least (worst) important components. Data were collected in an online platform called REDcap and analyzed using R software version 4.4.2\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e\"Feedback with an improvement plan\" was the highest-ranked component (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \"Feedback with goals/targets\" was the second-ranked component (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \"Delivery method of feedback\" was the third-ranked feedback component (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \"Feedback with peer comparison\", the \"Format of the feedback report\", and the \"Source of feedback\" had negative mean scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0217, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3408 respectively). \"The Feedback recipient\" component had the smallest mean score. This indicates that these are less important feedback components than others.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings from this study have found that the most preferred feedback components among health workers are \"Feedback with an Improvement Plan,\" \"Feedback with Goals/Targets,\" and \"Delivery Method of Feedback\". This suggests that they need feedback which is actionable, Goals oriented and well delivered. The findings therefore have given us ways to improve the technique we use to give feedback, enhance guidelines adherence, and, therefore, improve the quality of primary healthcare services.\u003c/p\u003e","manuscriptTitle":"Optimizing Audit and Feedback in Low-Resource Settings: A Best-Worst Scaling Study of Healthcare Workers Preference in Zanzibar","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 14:29:31","doi":"10.21203/rs.3.rs-6270141/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":"0d5d3537-9157-42f6-8603-f41fbddfd6ba","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-01T05:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 14:29:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6270141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6270141","identity":"rs-6270141","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}