Health Provider or Debt Collector? The Unintended Consequences of Integrating Income-generating Activities with Community Health Interventions in Kenya
Bibliographic record
Abstract
Abstract This study explores the challenges faced by community health promoters (CHPs) in Kenya as they attempt to couple income-generating activities with their public health duties. Drawing on qualitative data from in-depth interviews and focus groups discussions (FGDs) across three counties, we investigate why many CHPs ultimately abandoned health-related entrepreneurial ventures despite initial optimism. Our findings reveal two key challenges: (1) a volatile institutional environment created by shifting government policies and nongovernmental organization (NGO) interventions that destabilized markets for health products, and (2) role conflicts arising from community expectations and eroding trust when commercial transactions were introduced into health service relationships. These tensions compromise household visitations potentially impeding universal health coverage efforts. Our findings contribute to research on necessity entrepreneurship by highlighting the critical importance of institutional stability and role compatibility when designing entrepreneurial initiatives in resource-constrained contexts. It also extends institutional theory in entrepreneurship by demonstrating how institutional complexity can create irreconcilable tensions rather than opportunities for hybrid organizing.
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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 | 0.003 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".