Implementation framework for income generating activities identified by community health volunteers (CHVs): a strategy to reduce attrition rate in Kilifi County, Kenya
Bibliographic record
Abstract
BACKGROUND: Despite the proven efficacy of Community Health Volunteers (CHVs) in promoting primary healthcare in low- and middle-income countries (LMICs), they are not adequately financed and compensated. The latter contributes to the challenge of high attrition rates observed in many settings, highlighting an urgent need for innovative compensation strategies for CHVs amid budget constraints experienced by healthcare systems. This study sought to identify strategies for implementing Income-Generating Activities (IGAs) for CHVs in Kilifi County in Kenya to improve their livelihoods, increase motivation, and reduce attrition. METHODS: An exploratory qualitative research study design was used, which consisted of Focus group discussions with CHVs involved in health promotion and data collection activities in a local setting. Further, key informant in-depth interviews were conducted among local stakeholder representatives and Ministry of Health officials. Data were recorded, transcribed and thematically analysed using MAXQDA 20.4 software. Data coding, analysis and presentation were guided by the Okumus' (2003) Strategy Implementation framework. RESULTS: A need for stable income was identified as the driving factor for CHVs seeking IGAs, as their health volunteer work is non-remunerative. Factors that considered the local context, such as government regulations, knowledge and experience, culture, and market viability, informed their preferred IGA strategy. Individual savings through table-banking, seeking funding support through loans from government funding agencies (e.g., Uwezo Fund, Women Enterprise Fund, Youth Fund), and grants from corporate organizations, politicians, and other donors were proposed as viable options for raising capital for IGAs. Formal registration of IGAs with Government regulatory agencies, developing a guiding constitution, empowering CHVs with entrepreneurial and leadership skills, project and group diversity management, and connecting them to support agencies were the control measures proposed to support implementation and enhance the sustainability of IGAs. Group-owned and managed IGAs were preferred over individual IGAs. CONCLUSION: CHVs are in need of IGAs. They proposed implementation strategies informed by local context. Agencies seeking to support CHVs' livelihoods should, therefore, engage with and be guided by the input from CHVs and local stakeholders.
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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 | 0.038 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".