Is the process for selecting indigents to receive free care in Burkina Faso equitable?
Bibliographic record
Abstract
BACKGROUND: In Burkina Faso, patients are required to pay for healthcare. This constitutes a barrier to access for indigents, who are the most disadvantaged. User fee exemption systems have been created to facilitate their access. A community-based initiative was thus implemented in a rural region of Burkina Faso to select the worst-off and exempt them from user fees. The final selection was not based on pre-defined criteria, but rather on community members' tacit knowledge of the villagers. The objective of this study was to analyze the equitable nature of this community-based selection process. METHOD: Based on a cross-sectional study carried out in 2010, we surveyed 1,687 indigents. The variables collected were those that determine healthcare use according to the Andersen-Newman model (1969): sociodemographic variables; income; occupation; access to financial, food or instrumental assistance; presence of chronic illness; and disabilities related to vision, muscle strength, or mobility. Bivariate analyses and logistic regression were performed. RESULTS: User fee exemptions were given mainly to indigents who were widowed (OR = 1.40; CI 95% [1.10-1.78]), had no financial assistance from their household for healthcare (OR = 1.58; CI 95% [1.26-1.97], lived alone (OR = 1.28; CI 95% [1.01-1.63]), lived with their spouses, (OR = 2.00; CI 95% [1.35-2.96], had vision impairments (OR = 1.45; CI 95% [1.14-1.84]), or had poor muscle strength and good mobility (OR = 1.73; CI 95% [1.28-2.33]). The indigent selection was not determined by household income, self-reported chronic illness, or previous use of services. CONCLUSION: The community selection process took into account factors related to social vulnerability and functional limitations. However, we cannot affirm that the selection process was perfectly equitable, as it was very restrictive due to the limited budget available and the State's lack of engagement in this matter. Exemption processes should be temporary solutions, and the State should make a commitment to move toward universal healthcare coverage.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".