How community-based health workers fulfil their roles in epidemic disease surveillance: a case study from Burkina Faso
Bibliographic record
Abstract
BACKGROUND: In 2016, Burkina Faso adopted a new community-based model for disease surveillance, appointing two community-based health workers (CBHWs) per village. The CBHWs play a crucial yet under-researched role in Burkina Faso's health system. This study aimed to analyze the factors influencing their practices in relation to their official roles in epidemic disease surveillance. METHODS: Conducted in the Dandé Health District in southwestern Burkina Faso, this qualitative study collected data through semi-structured interviews with 15 CBHWs and 25 health professionals, supplemented by observations of the CBHWs' working conditions. Data analysis employed a qualitative content analysis. RESULTS: Analysis showed major challenges in the current community health strategy, particularly in capacity building and the working conditions of CBHWs (e.g., lack of monthly report sheets and financial incentives). Recognition from the community was the key motivation for volunteering as a CBHW in Dandé Health District where rural populations are under great financial pressure. Consequently, financial incentives (monthly remuneration and extra incentives) and non-financial rewards in terms of status and prestige, play a crucial role in sustaining volunteer enegagment and effectiveness. CONCLUSIONS: This study underscores the necessity of establishing a clear policy on compensation and protection for CBHWs to motivate and optimize their work. Such policies are essential for enhancing their contribution to a robust national community surveillance system, ultimately improving public health outcomes in Burkina Faso.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".