RESUMEN
Introduction: The family physician programme (FPP) was implemented nearly two decades ago as a major health reform. Since the health system and FPP function in a rapidly changing social and economic environment, successful expansion of the programme requires a detailed analysis of its multiple major challenges, including the crucial aspect of its funding system. This systematic review aimed to assess the challenges in the FPP relative to its financing. Method: All published articles related to the FPP in Iran were included in this study. In particular, original qualitative studies published in English or Persian from 2011 to 2021 were included. In January 2022, international credible scholarly databases and Persian databases were searched. All selected articles were carefully studied, and the data were extracted using the sample, phenomenon of interest, design, evaluation and research type technique. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were used in preparing the study report. Results: Among 491 articles retrieved from the search strategy, 50 met the inclusion criteria after their titles and abstracts were screened. Twenty-nine studies were excluded after their full texts were reviewed. A total of 11 eligible empirical studies were finally included. Based on the results, six broad categories (budget and funding, insurance system, tariffs, payments, accountability and injustice) were identified as financial challenges. Conclusion: This study identified the challenges associated with financing among family physicians, and the results could provide guidance for policy-making in the expansion of the FPP.
RESUMEN
This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.