Application of the Ultra-Poverty Graduation Model in understanding community health volunteers’ preferences for socio-economic empowerment strategies to enhance retention: a qualitative study in Kilifi, Kenya
Bibliographic record
Abstract
BACKGROUND: A significant shortage of healthcare workforce exists globally. To achieve Universal Healthcare coverage, governments need to enhance their community-based health programmes. Community health volunteers (CHVs) are essential personnel in achieving this objective. However, their ability to earn a livelihood is compromised by the voluntary nature of their work; hence, the high attrition rates from community-based health programmes. There is an urgent need to support CHVs become economically self-reliant. We report here on the application of the Ultra-Poverty Graduation (UPG) Model to map CHVs' preferences for socio-economic empowerment strategies that could enhance their retention in a rural area in Kenya. METHODS: This study adopted an exploratory qualitative approach. Using a semi-structured questionnaire, we conducted 10 Focus Group Discussions with the CHVs and 10 Key Informant Interviews with County and Sub-county Ministry of Health and Ministry of Agriculture officials including multi-lateral stakeholders' representatives from two sub-counties in the area. Data were audio-recorded and transcribed verbatim and transcripts analysed in NVivo. Researcher triangulation supported the first round of analysis. Findings were mapped and interpreted using a theory-driven analysis based on the six-step Ultra-Poverty Graduation Model. RESULTS: We mapped the UPG Model's six steps onto the results of our analyses as follows: (1) initial asset transfer of in-kind goods like poultry or livestock, mentioned by the CHVs as a necessary step; (2) weekly stipends with consumption support to stabilise consumption; (3) hands-on training on how to care for assets, start and run a business based on the assets transferred; (4) training on and facilitation for savings and financial support to build assets and instil financial discipline; (5) healthcare provision and access and finally (6) social integration. These strategies were proposed by the CHVs to enhance economic empowerment and aligned with the UPG Model. CONCLUSION: These results provide a user-defined approach to identify and assess strategic needs of and approaches to CHVs' socio-economic empowerment using the UPG model. This model was useful in mapping the findings of our qualitative study and in enhancing our understanding on how these needs can be addressed in order to economically empower CHVs and enhance their retention in our setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".