Community health volunteers challenges and preferred income generating activities for sustainability: a qualitative case study of rural Kilifi, Kenya
Bibliographic record
Abstract
BACKGROUND: There is a global emphasis on engaging community health volunteers (CHVs) in low- to middle-income countries (LMICs) to reach to the vast underserved populations that live in rural areas. Retention of CHVs in most countries has however been difficult and turnover in many settings has been reported to be high with profound negative effects on continuity of community health services. In rural Kenya, high attrition among CHVs remains a concern. Understanding challenges faced by CHVs in rural settings and how to reduce attrition rates with sustainable income-generating activities (IGAs) is key to informing the implementation of contextual measures that can minimise high turnover. This paper presents findings on the challenges of volunteerism in community health and the preferred IGAs in rural Kilifi county, Kenya. METHODS: The study employed qualitative methods. We conducted 8 key informant interviews (KIIs) with a variety of stakeholders and 10 focus group discussions (FGDs) with CHVs. NVIVO software was used to organise and analyse our data thematically. RESULTS: Community Health Volunteers work is not remunerated and it conflicts with their economic activities, child care and other community expectations. In addition, lack of supervision, work plans and relevant training is a barrier to delivering CHVs' work to the communities. There is a need to remunerate CHVs work as well as provide support in the form of basic training and capital on entrepreneurship to implement the identified income generating activities such as farming and events management. CONCLUSIONS: Strategies to support the livelihoods of CHVs through context relevant income generating activities should be identified and co-developed by the ministry of health and other stakeholders in consultation with the CHVs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".