User fees exemptions alone are not enough to increase indigent use of healthcare services
Bibliographic record
Abstract
The aim of this study was to assess whether user fees exemptions increased healthcare services use among indigents in the Ouargaye district in Burkina Faso. In this pre-post study, we surveyed 1224 indigents in 2010 about their healthcare services use over the preceding 6 months. Of these, 540 subsequently received a user fees exemption card. A follow-up survey was conducted 1 year later with a 55.3% retention rate. Analyses were performed in accordance with Andersen and Newman's model (Societal and individual determinants of medical care utilization in the United States. Milbank Q 1973;51:95-124) to explain healthcare services use by considering predisposing and facilitating factors and health needs indicators. Logistic regression analyses were performed.Among indigents exempted from user fees, 46.2% increased their healthcare services use in 2011, as opposed to 42.1% among the non-exempted. Being exempted was not associated with increased use of services (odds ratio, OR = 1.1, 95% confidence interval, CI [0.80-1.51]). Regardless of whether they were exempted or not, the indigents most likely to have increased their healthcare services use were older than 69 years of age (OR = 1.66, 95% CI [1.05-2.64]), male (OR = 1.44, 95% CI [0.99-2.08]), in low-income households (OR = 1.71, 95% CI [1.15-2.54]), and had received financial support from their families to obtain healthcare (OR = 1.59, 95% CI [1.1-2.28]). The indigents' increased healthcare services use was not attributable to user fees exemptions. Some contamination of the intervention is conceivable. Interventions combining user fees exemptions with actions targeting other obstacles to healthcare access would probably be more effective in increasing indigents' use of healthcare centres.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".