Application Of The Ultra-Poverty Graduation Model In Understanding Community Health Volunteers’ Preferences For Socio-Economic Empowerment Strategies: A Qualitative Study In Kilifi, Kenya
Bibliographic record
Abstract
Abstract Background: There is a significant shortage of healthcare workforce globally. In order to achieve Universal Healthcare coverage, governments need to enhance their community-based healthcare provider programmes. Community health volunteers (CHVs) are essential personnel in achieving this objective, however their needs remain unmet hence their high attrition rates.Methods: This study adopted an exploratory mixed methods qualitative approach including Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs). Using a semi-structured questionnaire, out of the 17 Community Health Units (CHUs), we conducted 10 FGDs based on the number of CHUs in each of the two sub-counties, three from Rabai Sub-county and seven from Kaloleni Sub-county. We conducted 10 key-informant interviews from participants who included County and sub -county Ministry of Health (MOH) and Ministry of Agriculture (MOA) officials as well as multi-lateral stakeholders’ representatives from Kaloleni and Rabai sub-counties. Data was audio-recorded and transcribed verbatim. Transcripts were analysed using NVivo qualitative data software version 10. Researcher triangulation supported the first round of analysis of the data in this study. Data was mapped and findings interpreted using a theory driven analysis based on the Ultra-Poverty Graduation (UPG) model. Results: The results are presented using the Ultra Poverty Graduation (UPG) model, which involves a six-step intervention. It consists of the provision of asset transfer of an in-kind good such as poultry or livestock, weekly stipends with consumption support to stabilize consumption, hands-on training on how to care for assets and run a business, savings and financial support to build assets and instil financial discipline, healthcare provision and access and finally social integration.Conclusion: The results of this study provides a user-identified approach to identify and assess the strategic needs of CHVs for socio-economic empowerment. The study further applies a sustainable economic empowerment model to provide further understanding on how these needs can be addressed in order to enhance retention of CHVs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".