Background: The Ethiopian health extension program (HEP) is an innovative community-based strategy aimed at disease prevention and health promotion. While health extension workers (HEWs) are its front-line workers, the involvement of clinicians remains an integral part. The goals of this study were to: (1) assess the correlation of clinician attitude with predictors and (2) assess the reliability and validity of the survey instrument. Methods: A cross-sectional study design was utilized to collect data from a sample of 1239 clinicians using 28 items of attitude questions. Exploratory factor analysis (EFA) was applied to create the latent variables. Oblique Promax type rotation with factor loading (> 0.5) was used. Cronbach’s alpha was used to assess reliability, with a level of > 0.7 suggesting good reliability. Confirmatory factor analysis (CFA) was undertaken, with the values of Root Mean Square Error Administration (RMSEA) < 0.08, Standardized Root Mean Square Residual (SRMR) < 0.05, comparative fit index (CFI) 0.9–0.95, and Tucker-Lewis index (TLI) 0.9–0.95 suggesting acceptable model fit. A linear regression analysis was conducted. Results: EFA produced two latent variables which explained 93.2% of the total variance. The latent variables were labeled as perceived attitude towards the skill of HEWs (F1), and perceived attitude towards the impact of HEP (F2). Internal reliability for the 28 items was reported with a Cronbach’s alpha of 0.94, and for F1 and F2 it was 0.91 and 0.90, respectively. CFA was done and RMSEA was reported at 0.04, SRMR was 0.03, and CFI and TLI were each 0.97. The value of clinician attitude increased by 3.5, 95% CI (1.5, 5.3), P-value < 0.001 for those who have been exposed to the HEP program than non-exposed. Similarly, clinician attitude was lower for degree holders compared to those with diplomas by − 2.7, 95% CI (− 4.4, − 0.94), P-value < 0.002. Conclusion: Clinician attitude increased as exposure to HEP increased. Clinician attitude towards HEP has two latent variables. Furthermore, the assessment tool demonstrated good reliability and validity. In conclusion, it is worthy valued for clinicians to receive orientation about HEP, and researchers and program evaluators can use this assessment tool.

Background: The Health Extension Program (HEP) is Ethiopia’s flagship community health program, launched in 2003. Health Extension Workers (HEWs) are key vehicles for the delivery of the HEP. While it is believed that there is high attrition among HEWs, the magnitude of or reasons for attrition is unknown. Their intention to leave their jobs in the next 5 years has also never been investigated on a national scale. This study aimed to assess the magnitude of, and factors affecting HEWs’ attrition and intention to leave in Ethiopia. Methods: The study used mixed methods to address the research objectives. Using stratified random sampling and regions as strata, 85 districts from nine regions were randomly selected in Ethiopia. Within each study district, six kebeles (village clusters) were randomly selected, and all HEWs working in these kebeles were interviewed to capture their 5-year intention to leave. The study team developed a data-extraction tool for a rapid review of district-level documents covering the period June 30, 2004 through June 30, 2019 to gather their attrition figures. We used survival analysis to model attrition data and checked model goodness-of-fit using the Cox–Snell residual test. We additionally collected qualitative data from HEWs who had left their positions. Results: The attrition of HEWS over the lifespan of the HEP was 21.1% (95% CI 17.5–25.3%), and the median time to exit from HEWs workforce was 5.8 years. The incidence rate was 3.1% [95% CI 2.8–3.4]. The risk of attrition was lower amongst HEWs with level four certifications, with children, and among those working in urban settings. By contrast, HEWs who were not certified with a certificate of competency (COC), who were deployed after 2008, and those who were diploma/degree holders were more likely to exit the HEWs workforce. The magnitude of intention to leave was 39.5% (95% CI 32.5–47%) and the primary reasons to leave were low incentives, dearth of career development opportunities (50.8%), high workload (24.2%), and other psychosocial factors (25%). Conclusion: Although the magnitude of attrition is not worryingly high, we see high magnitude in HEWs’ intention to leave, indicating a dissatisfied workforce. Multiple factors have contributed to attrition and intention to leave, the prevalence of many of which can be reduced to fit the needs of this workforce and to retain them for the sustained delivery of primary healthcare in the country. Ensuring HEWs’ job satisfaction is important and linked with their career development and potentially higher rates of retention.

Health extension workers (HEWs) are the primary implementers of the health extension program (HEP) – a government-led community-based health-care program, but clinicians are anticipated to play a critical part in providing extensive support, thus, this study aimed to assess clinicians’ knowledge and perceptions of HEP, a cross-sectional study was conducted with 1239 clinicians. A set of 54-item questions was created based on HEP guidelines and relevant literature. Exploratory factor analysis (EFA) identified latent variables with Eigenvalues matrix >1. Cluster variables were derived through orthogonal varimax factor rotation, and internal reliability was evaluated using Cronbach’s alpha coefficient. A composite score was generated for each constructed factor, and the associations between variables were determined using a one-way analysis of variance. Multiple linear regression analysis was conducted to eliminate confounders, with statistical significance set at P < 0.05. EFA provides three factors explaining 91% of the total variance and labeled as “clinician-perceived attitude towards the skill of HEWs” (F1), “clinician knowledge on HEP activities” (F2), and “clinician-perceived attitude towards the impact of HEP” (F3). Internal reliability for the 54 items was 0.96, and it was 0.93, 0.90, and 0.89 for F1, F2, and F3, respectively. 75.5% and 76.2% of clinicians had favorable attitudes toward F1 and F3, respectively, and 70.2% had good knowledge of F2. F1 was positively correlated with participation in HEP review meetings, home visits, HEP outreach, and willingness to work in HEP but negatively correlated with degree holders. F2 was positively correlated with participation in HEP review meetings, home visits, being married, and non-medical doctors but negatively correlated with willingness to work in HEP, older age, female, and degree holder clinicians. Three factors, focusing on knowledge, skill, and the impact of HEP, were found and fall under the second-generation HEP framework. Therefore, strengthening HEP guidelines is essential to ensuring the delivery of sustainable and pro-poor HEP.

Soil organic carbon (SOC) is a key component of terrestrial ecosystems, serving as an energy source for soil microorganisms and playing an essential role in climate regulation and ecosystem productivity. However, SOC stocks are highly influenced by land-use and land-cover changes. This study aims to estimate and map SOC using multispectral Sentinel-2 and RapidEye imagery combined with environmental, soil, and topographic variables across Khat-dominated landscapes in the Haramaya district, eastern Ethiopia. A total of 88 soil samples were collected and analyzed in the laboratory for organic carbon estimation. Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGB), were applied to predict SOC, and their performances were evaluated using the coefficient of determination (R²), root mean square error (RMSE and mean absolute error (MAE). Laboratory-measured SOC values ranged from 0.83% to 3.9%. Both satellite datasets produced comparable predictions, with Sentinel-2 estimating slightly higher mean SOC values (~40mg/ha), while RapidEye(~38mg/ha) provided more spatially detailed and accurate maps due to its finer resolution. Among the algorithms tested, RF outperformed XGB, showing higher predictive accuracy and stability, particularly under heterogeneous landscape conditions. The results suggest that both Sentinel-2 and RapidEye data are suitable for SOC estimation and mapping, with higher-resolution imagery preferred for detailed spatial analysis. Future research should focus on optimising predictor selection and assessing the potential impacts of Khat cultivation on SOC variability and spatial distribution.