Low coverage but few inclusion errors in Burkina Faso: a community-based targeting approach to exempt the indigent from user fees
Bibliographic record
Abstract
BACKGROUND: User fees were generalized in Burkina Faso in the 1990s. At the time of their implementation, it was envisioned that measures would be instituted to exempt the poor from paying these fees. However, in practice, the identification of indigents is ineffective, and so they do not have access to care. Thus, a community-based process for selecting indigents for user fees exemption was tested in a district. In each of the 124 villages in the catchment areas of ten health centres, village committees proposed lists of indigents that were then validated by the health centres' management committees. The objective of this study is to evaluate the effectiveness of this community-based selection. METHODS: An indigent-selection process is judged effective if it minimizes inclusion biases and exclusion biases. The study compares the levels of poverty and of vulnerability of indigents selected by the management committees (n = 184) with: 1) indigents selected in the villages but not retained by these committees (n = 48); ii) indigents selected by the health centre nurses (n = 82); and iii) a sample of the rural population (n = 5,900). RESULTS: The households in which the three groups of indigents lived appeared to be more vulnerable and poorer than the reference rural households. Indigents selected by the management committees and the nurses were very comparable in terms of levels of vulnerability, but the former were more vulnerable socially. The majority of indigents proposed by the village committees who lived in extremely poor households were retained by the management committees. Only 0.36% of the population living below the poverty threshold and less than 1% of the extremely poor population were selected. CONCLUSIONS: The community-based process minimized inclusion biases, as the people selected were poorer and more vulnerable than the rest of the population. However, there were significant exclusion biases; the selection was very restrictive because the exemption had to be endogenously funded.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".