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Background There is considerable optimism in mHealth’s potential to overcome health system deficiencies, yet gender inequalities can weaken attempts to scale-up mHealth initiatives. We report on the gendered experiences of an mHealth intervention, in Southern Ethiopia, realised by the all-female cadre of Health Extension Workers (HEWs). Methodology Following the introduction of the mHealth intervention, in-depth interviews (n = 19) and focus group discussions (n = 8) with HEWs, supervisors and community leaders were undertaken to understand whether technology acted as an empowering tool for HEWs. Data was analysed iteratively using thematic analysis informed by a socio-ecological model, then assessed against the World Health Organisation’s gender responsive assessment scale. Results HEWs reported experiencing: improved status after the intervention; respect from community members and were smartphone gatekeepers in their households. HEWs working alone at health posts felt smartphones provided additional support. Conversely, smartphones introduced new power dynamics between HEWs, impacting the distribution of labour. There were also negative cost implications for the HEWs, which warrant further exploration. Conclusion MHealth has the potential to improve community health service delivery and the experiences of HEWs who deliver it. The introduction of this technology requires exploration to ensure that new gender and power relations transform, rather than disadvantage, women. Keywords communities, e-health, gender.
In Ethiopia, the Health Extension Programme (HEP), initiated in 2004, is a free primary health care package in which 38 000 female HEWs offer 16 essential health packages.20–22 HEWs are salaried government employees who have completed at least grade ten. They are selected by their communities to complete one year of training in basic health service delivery. A health post serves a population of about 5000 and is staffed by two HEWs accountable to the kebele (lowest administrative unit). HEWs are supported by female volunteers, known as the ‘Health Development Army’18 and supervised by health professionals from health centres. Health centres in turn, are overseen by the woreda (district) health office (Fig. (Fig.22). Adapted from the WHO Gender Responsive Assessment Scale: WHO, (2011). Gender mainstreaming for health managers: a practical approach. Geneva. In spite of low ICT access and usage compared with other African countries, the Ethiopian Federal Ministry of Health has embraced mHealth in its national strategic health plan.23 Ethiopia prioritizes maternal health services and calls for improved HEW performance on maternal health-related tasks.20,21,24 An mHealth intervention that focussed on the priority areas of TB and maternal health services19 and linked to the Ethiopian Ministry of Health’s mHealth strategic framework was conducted in Sidama zone, Southern Ethiopia, with a population of about 3.7 million. Our research, undertaken in six Primary Health Care Units across six districts, worked closely with and was realized by HEWs, their supervisors, health workers based at the catchment health centres and policy makers at woreda health office and zonal health department. One smartphone, assigned to each health post, was shared between two HEWs, who used the phone to input data on expectant mothers and TB. The data was uploaded to the HMIS where it was instantly available to other levels of the health system. Reminder messages prompted HEWs to follow-up on expectant mothers’ due dates and sputum examination for TB symptomatic cases. Ninety-seven smartphones and eight computers were distributed to HEWs, their supervisors, health centre staff and focal persons from district and zonal levels. Ongoing theoretical and practical training was conducted and a monthly airtime allowance of 100 birr (3.64 USD) was provided for the first five months. Subsequent top-ups were paid for by HEWs. Ethics was approved by the Liverpool School of Tropical Medicine16–22 and by the Ethiopian Ministry for Science and Technology in June 2016, and supported by the Regional Health Bureau. All participants gave written informed consent. Qualitative methods were used to generate rich insights into participants’ experiences of the intervention.25 They included face-to-face semi-structured in-depth interviews (IDIs, n = 19) and single sex focus group discussions (FGDs, n = 8) with HEWs, supervisors and community leaders (Table (Table1).1). (In the study districts, all HEWs are female and all community leaders male. Supervisors are predominantly male. Disaggregating by gender and district would breech confidentiality.) Interview topic guides explored the gendered elements of the intervention; ways in which the mobile phones helped or hindered HEWs’ roles, how HEWs used the phones outside of work and the impact on their relationships. Analysis, informed by an adapted socio-ecological model, was designed to evaluate how the intervention impacted the interface position of the HEWs and to establish how the intervention fared along the WHO’s gender transformative scale. Interviews were conducted in four districts purposively selected for variation in geographic location and performance. Qualitative interviews conducted by participant and district aMerged with participants from District 3 due to geographical proximity and convenience of participants. bMerged with participants from District 4 due to geographical proximity and convenience of participants. In interviews, a local trained female research assistant, fluent in Sidamigna (the local dialect), ensured HEWs felt comfortable, and used topic guides to facilitate conversation. The lead researcher (RS) was on hand to clarify any questions or concerns. Interviews were conducted at health posts, health centres and woreda health offices, scheduled in private spaces, and recorded. These were transcribed and translated into English. Translation quality was reviewed (AZK). Qualitative analysis was done by reading and re-reading transcripts to identify iterative themes26 and select appropriate quotes (RS with inputs from AZK and DGD). Software NVivo was used to code and run queries on the data. Attention was paid to give voice to the majority and minority views.
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