ArticlePloS one2025
Community health volunteers' experiences during the COVID-19 pandemic in Kiambu county, Kenya: A qualitative study.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor already overburdened health systems in low- and middle-income countries (LMICs), the COVID-19 pandemic presented an almost impossible challenge. In Kenya, efforts to mitigate the impact of the pandemic included the mobilization of community health volunteers (CHVs), a cadre that has been historically understaffed and under-resourced. These volunteers were required to sustain the delivery of routine community-based health services while also taking on additional responsibilities related to COVID-19 mitigation. This study explored the challenges faced by CHVs during the COVID-19 response in Kiambu County; focusing on their experiences with control measures, impacts on community-level healthcare delivery, and perspectives on the government's pandemic response within the community health framework.
methodsThis study employed a phenomenological exploratory qualitative design. Due to logistical constraints, only two focus group discussions could be conducted with 24 CHVs, representing 48% of eligible participants who met the inclusion criteria: a minimum of five years of experience, active involvement in the pandemic response, and availability for the interview. Data were digitally recorded, transcribed, translated, and coded for thematic analysis.
resultsSpecific themes from the experiences of the CHVs during the COVID-19 lockdown were: (1) dedication and commitment to serving the community; (2) overcoming demoralization; and (3) community barriers to health care delivery and access. In regards to the community perspectives of COVID-19, the CHVs relayed widespread misinformation among community members, with experiences of stigma due to COVID-19 misconceptions. They also gave second-person accounts of the economic strife the community went through as a result of mitigation measures. The CHVs were skeptical in the county's preparedness in dealing with both the COVID-19 and future pandemics.
conclusionDespite facing stigma, misinformation, limited resources, and economic hardships, CHVs demonstrated commitment to their roles. Their efforts not only underscored their resilience but also revealed critical gaps in preparedness and resource allocation within the healthcare system. However, the small number of FGDs and insufficent data saturation should be considered when interpreting the findings. Nonetheless, our study provides a starting point for further research and comparative analysis across other counties in Kenya.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.