The impact of community-based health insurance on the utilization of medically trained healthcare providers among informal workers in Bangladesh
Bibliographic record
Abstract
We aimed to estimate the impact of a Community-Based Health Insurance (CBHI) scheme on utilization of healthcare from medically trained providers (MTP) by informal workers. A quasi-experimental study was conducted where insured households were included in the intervention group and uninsured households in comparison group. In total 1,292 (646 insured and 646 uninsured) households were surveyed from Chandpur district comprising urban and rural areas after 1 year period of CBHI introduction. Matching of the characteristics of insured and uninsured groups was performed using a propensity score matching approach to minimize the observed baseline differences among the groups. Multilevel logistic regression model, with adjustment for individual and household characteristics was used for estimating association between healthcare utilization from the MTP and insurance enrolment. The utilization of healthcare from MTP was significantly higher in the insured group (50.7%) compared to the uninsured group (39.4%). The regression analysis demonstrated that the CBHI beneficiaries were 2.111 (95% CI: 1.458-3.079) times more likely to utilize healthcare from MTP.CBHI scheme increases the utilization of MTP among informal workers. Ensuring such healthcare for these workers and their dependents is a challenge in many low and middle income countries. The implementation and scale-up of CBHI schemes have the potential to address this challenge of universal health coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".