Characterisation of the rural indigent population in Burkina Faso: a screening tool for setting priority healthcare services in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: In Africa, health research on indigent people has focused on how to target them for services, but little research has been conducted to identify the social groups that compose indigence. Our aim was to identify what makes someone indigent beyond being recognised by the community as needing a card for free healthcare. METHODS: We used data from a survey conducted to evaluate a state-led intervention for performance-based financing of health services in two districts of Burkina Faso. In 2015, we analysed data of 1783 non-indigents and 829 people defined as indigents by their community in 21 villages following community-based targeting processes. Using a classification tree, we built a model to select socioeconomic and health characteristics that were likely to distinguish between non-indigents and indigents. We described the screening performance of the tree using data from specific nodes. RESULTS: Widow(er)s under 45 years of age, unmarried people aged 45 years and over, and married women aged 60 years and over were more likely to be identified as indigents by their community. Simple rules based on age, marital status and gender detected indigents with sensitivity of 75.6% and specificity of 55% among those 45 years and over; among those under 45, sensitivity was 85.5% and specificity 92.2%. For both tests combined, sensitivity was 78% and specificity 81%. CONCLUSION: In moving towards universal health coverage, Burkina Faso should extend free access to priority healthcare services to widow(er)s under 45, unmarried people aged 45 years and over, and married women aged 60 years and over, and services should be adapted to their health needs. ETHICS CONSIDERATIONS: The collection, storage and release of data for research purposes were authorised by a government ethics committee in Burkina Faso (Decision No. 2013-7-066). Respondent consent was obtained verbally.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".