Can the financial burden of being a community health volunteer in western Kenya exacerbate poverty?
Bibliographic record
Abstract
Community health volunteers (CHVs) act as vital links between communities and health facilities, bridging the health service delivery gap common in low- and middle-income countries. In 2013, changes in funding in western Kenya left most CHVs without their individual monthly stipend. In this article, we explore how the implementation of a pooled incentive model had an impact on the lives of CHVs from two counties in western Kenya. Participation in this form of table banking was meant to allow CHVs to pool their resources together and invest in income-generating activities to offset the costs of unpaid health work. A pre-post qualitative study, consisting of focus group discussions and in-depth interviews explored CHV compensation, motivations and challenges experienced in 2013 and 2015, prior to and after the implementation of this pooled incentive model. Following withdrawal of the monthly stipend, we found that CHVs continued to take on roles and responsibilities of paid health workers, motivated by shared social identities and connections to their communities. However, replacing the stipend with a pooled-group incentive model seemingly exacerbated the financial burden already experienced by this vulnerable population. This study brings into question the sustainability and viability of a volunteer community health worker programme and highlights the need to address the financial burden associated with the CHV role in western Kenya.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".