The effect of a community-based health insurance on the out-of-pocket payments for utilizing medically trained providers in Bangladesh
Bibliographic record
Abstract
BACKGROUND: We aimed to estimate the effect of the community-based health insurance (CBHI) scheme on the magnitude of out-of-pocket (OOP) payments for the healthcare of the informal workers and their dependents. The CBHI scheme was piloted through a cooperative of informal workers, which covered seven unions in Chandpur Sadar Upazila, Bangladesh. METHODS: A quasi-experimental study was conducted using a case-comparison design. In total 1292 (646 insured and 646 uninsured) households were surveyed. Propensity score matching was done to minimize the observed baseline differences in the characteristics between the insured and uninsured groups. A two-part regression model was applied using both the probability of OOP spending and magnitude of such spending for healthcare in assessing the association with enrolment status in the CBHI scheme while controlling for other covariates. RESULTS: The OOP payment was 6.4% (p < 0.001) lower for medically trained provider (MTP) utilization among the insured compared with the uninsured. However, no significant difference was found in the OOP payments for healthcare utilization from all kind of providers, including the non-trained ones. CONCLUSIONS: The CBHI scheme could reduce OOP payments while providing better quality healthcare through the increased use of MTPs, which consequently could push the country towards universal health 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".