Optimize Audit and feedbaCk To Implement eVidence-based prAcTices in Primary Health carE

Researchers and experts will have a consultation meeting to generate the top 7 AnF intervention components, and a BWS survey will be employed to rank these components according to their importance and further select 3 potentially most effective and feasible components for effectiveness validation through the factorial trial in the next step. The BWS survey is a screening experiment, based on random utility theory, in which a trade-off mechanism is triggered by participants choosing the best and worst of a set of components or options, thereby quantifying the relative importance of each component and distinguishing the most salient among a set of important components. The BWS questionnaire will be developed and tested using a mixed-method approach based on the previous research results to obtain healthcare workers' prioritized acceptance of the different AnF components (relative importance) when deciding to improve the quality of care (completion rates of guideline entries), in order to further identify potentially the most important few components out of the range of components. The Balanced Incomplete Block Design (BIBD) is an experimental design used in BWS for improving results by organizing items into blocks and balancing the number of presentations of items across participants, which allows researchers to efficiently compare a set of items with each other. BIBD ensures equal number of times of occurrence for items in blocks and pairs items equally, reducing bias and increasing statistical precision of ratings. BIBD is especially valuable with a larger number of ranked items. The investigators will use BIBD in BWS in a typical way, by dividing items randomly into subsets (i.e. blocks) and assigning a questionnaire with all blocks to each participant, ensuring robust preference rankings. The investigators will use the %MktBSize macro in SAS 9.4 software to realize BIBD for questionnaire development. In our study, for the 7 AnF intervention components (treatments), the investigators will have 7 different blocks, each containing 3 AnF components (treatments). the investigators will invite participants to reflect on which AnF component of these 7 different blocks is most effective and which AnF component is least effective. The investigators will be giving 1 point when a component is chosen as most effective, and -1 when a component is chosen as least effective. Then, based on the standardized score of each component, the investigators will be finalizing the 3 most effective components from the BWS survey. In the optimization phase, a 2×2×2 factorial design (RCT) will be conducted, with three two-level components making up a total of 8 groups of AnF intervention. After obtaining consents from primary healthcare facilities and workers, all facilities will be randomly assigned to these 8 intervention groups. Then the investigators measure changes in healthcare quality from various audits and feedback in these facilities, and use statistical analysis to estimate main and interaction effects for AnF components on improving primary healthcare quality. The optimal AnF combination will be determined by considering effects and resource constraints in local implementation settings. The investigators assume that the following 3 AnF components with 2 levels each are selected from the BWS survey. 1. Source of Feedback: Level 1: Researchers Level 2: Authoritative Bodies 2. Feedback with Peer Comparison: Level 1: Yes (Peer Comparison) Level 2: No (No Peer Comparison) 3. Delivery Method of Feedback: Level 1: Face to face Level 2: Electronic Mail After deciding the 8 AnF intervention groups, the investigators will invite primary healthcare workers, policymakers, and health system administrators to discuss feasible and operable details to conduct the 8 AnF interventions at primary healthcare facilities in the four different LMICs. The audited results of quality of care of the facility will be fed back to the healthcare workers of the facility, and then the outcome indicators reflecting effectiveness of AnF will be measured.

Inclusion Criteria: * In Mozambique, the inclusion criteria are nurses, technicians of general medicine and doctors in public primary healthcare facilities. * In Zanzibar, Tanzania, the inclusion criteria are clinical officers, nurses and doctors in public primary healthcare facilities. * In Nepal, the inclusion criteria are doctors, health assistants, and senior auxiliary health workers in public primary healthcare facilities. * In China, the inclusion criteria are doctors practicing in public primary healthcare facilities. Exclusion Criteria: -The exclusion criteria are endocrinologists or diabetes specialists, interns, or students working during the time of visit.