Employer-sponsored Health Insurance (ESHI) Scheme- A New Model of Health Micro Insurance for the Garment Workers in Bangladesh
Bibliographic record
Abstract
Organized universal health coverage has not yet been introduced in most developing countries, including Bangladesh. Private health care is affordable only to the high-income group. The aim of this retrospective observational study was to evaluate health service coverage with disease prevention, access to health information, and cost control through health financing to help take appropriate decisions for the betterment of garment workers in Bangladesh. The study was conducted in seven readymade garments (RMG) factories in the Gazipur district from 24 April 2014 to 23 April 2015.A total of 9717 workers aged 18 to 60 years and belonging to the lowest salary groups were included in this Employer-Sponsored Health Insurance (ESHI) scheme. This new model of Health Micro Insurance (HMI) had treatment, laboratory facilities, health education, and medicine supply. The annual coverage for treatment cost was up to 15000 Bangladeshi taka (BDT) or US$192.8 and the premium for enrolment in the scheme was 487 BDT (US$6.3). An agreement foresaw that the surplus cost accrued was equally shared between the insurance company and the factory owner. A common software was used to generate and view all medical information. A total of 4524 (46.6%) workers (60.5% male and 39.5% female) received treatment. The participant's mean age was 28.3 years. The mean consultancy rate was 4.7 times. Participants mostly suffered from gastrointestinal problems(24.4%), and most prescribed medications were anti-ulcer drugs. The median value of drug cost, investigation cost, consultancy fees, and total medical cost were 126 (1.49 USD), 315 (3.71 USD), 200 (2.36 USD), and 734 BDT (8.66), respectively. The annual net premium paid by the factory owner was 4094504 BDT (48744 USD), and the total healthcare cost accrued was 5230156 BDT (62263 USD). This ESHI scheme is a better option for HMI for making healthcare accessible to the largest RMG sector in a developing country like Bangladesh.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".