This study evaluates an Empowerment Agency Training (EAT) intervention within the SPHERES programme that aims to strengthen personal agency among workers in primary healthcare centers (Puskesmas) in Indonesia. The intervention focuses on building self-efficacy, behavioural control, leadership, and intentional decision-making through structured training, follow-up action planning, observational support, and sustainability-oriented incentives. Strengthening personal agency is expected to improve the use of data for decision-making and the delivery of priority primary health services at the Puskesmas level.
This study evaluates a complex intervention in the form of an Empowerment Agency Training (EAT) implemented as an integral component of the SPHERES programme (NCT07126041) in Indonesia. The intervention is grounded in the recognition that, within digital health ecosystems, investments in technology and data systems have not consistently translated into improved service delivery due to limited individual capacity, behavioural adaptation, and organisational readiness among health workers. The EAT intervention therefore focuses on strengthening personal agency among Puskesmas staff, defined as the capacity to act intentionally, regulate behaviour, exercise leadership, and make informed decisions in complex work environments. The EAT intervention comprises four interrelated components delivered in an integrated and adaptive manner. First, empowerment through a personal agency approach is delivered via structured training sessions aimed at strengthening self-awareness, self-efficacy, leadership, assertiveness, and mastery of the work environment. Second, participants develop behaviour-related follow-up action plans that translate personal agency concepts into concrete and achievable changes in daily work practices, including communication, task management, teamwork, and the use of health data for decision-making. Third, expert shadowing provides direct observational support to understand real-world service dynamics. Fourth, sustainability-oriented incentives, including opportunities for advanced training and locally governed financial incentives, are used to reinforce engagement and support longer-term adoption of new practices. Within the SPHERES programme, the digitalisation of health information systems is deliberately complemented by interventions that address human and organisational factors. By embedding EAT within SPHERES, the intervention is designed to ensure that digital tools and data systems are actively interpreted, used, and translated into service improvements, rather than functioning solely as mechanisms for routine reporting. The intervention is evaluated using a cluster randomised controlled trial design, in which Puskesmas that are already participating in the SPHERES programme are randomly assigned to receive the additional Empowerment Agency Training (EAT) intervention. The study population includes all staff working at participating Puskesmas, with total population sampling applied at the facility level to ensure comprehensive exposure to the intervention and to minimise contamination. To assess service-related outcomes, selected health service users from Puskesmas catchment areas are also included. Community-level sampling for outcome assessment uses probability-based approaches proportional to population size. Primary outcomes include clinical data quality, including its completeness and submission timeliness, and changes in personal agency and health worker behaviour, particularly the use of health data for planning and decision-making. Additional outcomes include selected priority health service indicators at the Puskesmas level, such as coverage and quality of maternal, child, and non-communicable disease services. Outcome data are derived from validated personal agency survey instruments administered to health workers, as well as routine service data captured through digital health information systems integrated within the SPHERES platform. The evaluation adopts a convergent mixed-methods approach. Quantitative data are collected longitudinally before and after intervention implementation to assess changes in outcomes over time. Qualitative data from in-depth interviews, focus group discussions, and field observations are collected in parallel to examine contextual factors influencing implementation, mechanisms of change, acceptability, and sustainability of the intervention. Quantitative and qualitative findings are analysed separately and integrated during interpretation to provide a comprehensive understanding of intervention effects and implementation processes. At a broader level, this study is designed to generate evidence relevant to national health system development in Indonesia. By evaluating a complex, people-centred intervention embedded within a national digital health transformation programme, the findings are expected to inform policy decisions related to health workforce development, digital health scale-up, and the sustainable integration of system-level interventions into routine primary health care across nuanced settings.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
200
The intervention comprises a behavioral training program focused on personal agency and empowerment. The curriculum follows the established frameworks developed by The Self-Empowerment and Equity for Change (SEE Change) Initiative at the Johns Hopkins Bloomberg School of Public Health. The training is followed by simple, practical "prescription", or action plans, for behaviour change in daily work. Implementation is supported through direct observation by facilitators (expert shadowing) and incentives, including advanced training opportunities and locally managed financial support.
Purbalingga District Health Office
Purbalingga, Central Java, Indonesia
Lombok Barat District Health Office
Gerung, West Nusa Tenggara, Indonesia
Difference in clinical data quality between intervention and control arms, and changes in personal agency and health worker behaviour
Clinical data quality is determined by proportion of data entry in the digital health information system compared to the gold standard (legacy reporting system). Other metrics being the heaping index indicating digit preference in numeric clinical data input in various services, and also the proportion of biologically implausible values. Changes in personal agency and health worker behaviour is determined by the proportion of action derived from data-driven discussion, as well as the overall level of patient satisfaction from the care delivery.
Time frame: Up to 6 months
Completeness of care in maternal and neonatal care
* of pregnant women with at least six timely ANC services (K1 - K6) * of pregnant women with complete services per ANC encounter * of newborn with at least three timely PNC services (Kn1 - Kn3) * of mothers with newborn with at least four timely PNC services (Kf1 - Kf4) Neonatal mortality rate Stillbirth rate All indicators will be measured using the SPHERES dashboard.
Time frame: Up to 6 months
Completeness of care in tuberculosis treatment
* of TB suspect screened * of TB diagnosis from all screened suspects * of TB patients receiving medications * of TP patients being loss to follow-up * of TB patients with complete recovery All indicators will be measured using the SPHERES dashboard.
Time frame: Up to 6 months
Completeness of care in hypertensive treatment
* of people screened for hypertension * of people with hypertension diagnosis * of hypertensive patient receiving medications * of hypertensive patient with controlled blood pressure All indicators will be measured using the SPHERES dashboard.
Time frame: Up to 6 months
Completeness of care in diabetes treatment
* of people screened * of people with diabetes diagnosis * of diabetes patient receiving medications * of people with controlled HbA1c All indicators will be measured using the SPHERES dashboard.
Time frame: Up to 6 months
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