| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Penn-Kekana, Loveday Powell-Jackson, Timothy Haemmerli, Manon Dutt, Varun Lange, Isabelle L. Mahapatra, Aniva Sharma, Gaurav Singh, Kultar Singh, Sunita Shukla, Vasudha Goodman, Catherine |
| Abstract | Background A prominent strategy to engage private sector health providers in low- and middle-income countries is clinical social franchising, an organisational model that applies the principles of commercial franchising for socially beneficial goals. The Matrika programme, a multi-faceted social franchise model to improve maternal health, was implemented in three districts of Uttar Pradesh, India, between 2013 and 2016. Previous research indicates that the intervention was not effective in improving the quality and coverage of maternal health services at the population level. This paper reports findings from an independent external process evaluation, conducted alongside the impact evaluation, with the aim of explaining the impact findings. It focuses on the main component of the programme, the “Sky” social franchise. Methods We first developed a theory of change, mapping the key mechanisms through which the programme was hypothesised to have impact. We then undertook a multi-methods study, drawing on both quantitative and qualitative primary data from a wide range of sources to assess the extent of implementation and to understand mechanisms of impact and the role of contextual factors. We analysed the quantitative data descriptively to generate indicators of implementation. We undertook a thematic analysis of the qualitative data before holding reflective meetings to triangulate across data sources, synthesise evidence, and identify the main findings. Finally, we used the framework provided by the theory of change to organise and interpret our findings. Results We report six key findings. First, despite the franchisor achieving its recruitment targets, the competitive nature of the market for antenatal care meant social franchise providers achieved very low market share. Second, all Sky health providers were branded but community awareness of the franchise remained low. Third, using lower-level providers and community health volunteers to encourage women to attend franchised antenatal care services was ineffective. Fourth, referral linkages were not sufficiently strong between antenatal care providers in the franchise network and delivery care providers. Fifth, Sky health providers had better knowledge and self-reported practice than comparable health providers, but overall, the evidence pointed to poor quality of care across the board. Finally, telemedicine was perceived by clients as an attractive feature, but problems in the implementation of the technology meant its effect on quality of antenatal care was likely limited. Conclusions These findings point towards the importance of designing programmes based on a strong theory of change, understanding market conditions and what patients value, and rigorously testing new technologies. The design of future social franchising programmes should take account of the challenges documented in this and other evaluations. |
| Related Links | https://implementationscience.biomedcentral.com/counter/pdf/10.1186/s13012-018-0813-y.pdf |
| Ending Page | 15 |
| Page Count | 15 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17485908 |
| DOI | 10.1186/s13012-018-0813-y |
| Journal | Implementation Science |
| Issue Number | 1 |
| Volume Number | 13 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2018-09-24 |
| Access Restriction | Open |
| Subject Keyword | Health Services Research Public Health Health Informatics Health Policy Health Administration Health Promotion and Disease Prevention Process evaluation Multi-methods Social franchising Maternal health India |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Policy Public Health, Environmental and Occupational Health Health Informatics |
| Journal Impact Factor | 8.8/2023 |
| 5-Year Journal Impact Factor | 9.2/2023 |
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