Does mHealth influence community health worker performance in vulnerable populations? A mixed methods study in a multinational refugee settlement in Uganda
Bibliographic Data
| ID | 19594199 |
|---|---|
| Authors | Michael T Wagaba (0009-0002-8622-6993, Makerere University, corresponding author), David Musoke (0000-0003-3262-3918, Makerere University, corresponding author), Arthur Bagonza (0000-0002-2167-7294, Makerere University, corresponding author), John B Ddamulira (0009-0001-2549-5228), Christine Kayemba Nalwadda (0000-0002-9145-4854, Makerere University, corresponding author), Christopher Garimoi Orach (0000-0002-5209-5529, Makerere University, corresponding author) |
| Editors | Julia Robinson (0000-0003-0719-3995) |
| Year | 2023 |
| Volume | 3 |
| Issue | 12 |
| Pages | e0002741 |
| Publication date | 2023-12-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PLOS Global Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2767-3375 • E-ISSN: 2767-3375 |
| Publisher | Public Library of Science (PLoS) (PUBLISHER) |
| DOI | 10.1371/journal.pgph.0002741 |
| PMID | 38157328 |
| OpenAlex | W4390402491 |
| Language | EN |
| Citations received | 1 |
| References cited | 45 |
Community Health Workers (CHWs) provide healthcare in under-served communities, including refugee settlements, despite various challenges hindering their performance. Implementers have adopted mobile wireless technologies (m-Health) to improve the performance of CHWs in refugee settlements. We assessed the CHWs’ performance and associated factors in a multi-national refugee settlement, operating mHealth and paper-based methods. This cross-sectional study employed quantitative and qualitative data collection methods. Data for 300 CHWs was collected from implementing partners’ (IPs) databases. Nine focus group discussions (FGDs) with the CHWs and community members, two in-depth interviews (IDIs) with CHW leaders, and eight key informant interviews (KIIs) with six IPs and two local leaders were conducted. The qualitative data were analysed thematically using AtlasTi version 9 while the quantitative data were analysed at the univariate, bivariate and multivariable levels using Stata version14. The study found that only 17% of the CHWs performed optimally. The factors that significantly influenced CHW performance included education level: secondary and above (APR: 1.83, 95% CI: 1.02–3.30), having a side occupation (APR: 2.02, 95% CI: 1.16–3.52) and mHealth use (APR: 0.06, 95% CI: 0.02-.0.30). The qualitative data suggested that performance was influenced by the number of households assigned to CHWs, monetary incentives, adequacy of materials and facilitation. Particularly, mHealth was preferred to paper-based methods. Overall, the CHWs’ performance was sub-optimal; only 2 in 10 performed satisfactorily. The main factors that influenced performance included the level of education, use of mHealth, having another occupation, workload and incentivisation. CHWs and IPs preferred mHealth to paper-based methods. IPs should work to improve refugee settlement working conditions for the CHWs and adopt mHealth to improve CHW performance
Business · Data collection · Environmental health · Focus group · Geography · Health care · Incentive · mHealth · Political science · Psychological intervention · Qualitative property · Qualitative research · Refugee · Sociology · Computer Science · Global Maternal and Child Health · Health Literacy and Information Accessibility · Medicine · Mobile Health and mHealth Applications · Nursing · Psychology · Marketing
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |