Determinants of enrollment of informal sector workers in cooperative based health scheme in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Providing access to affordable health care for the informal sector remains a considerable challenge for low income countries striving to make progress towards universal health coverage. The objective of the study is to identify the factors shaping the decision to enroll in a cooperative based health scheme for informal workers in Bangladesh and also help to identify the features of informal workers without health schemes and their likelihood of being insured. METHODS: Data were derived from a cross-sectional in-house survey within the catchment area of a cooperative based health scheme in Bangladesh during April-June 2014, covering a total of 784 households (458 members and 326 non-members). Multivariate logistic regression model was used to identify factors associated with cooperative based health scheme and explanatory variables. FINDINGS: This study found that a number of factors were significant determinants of health scheme participation including sex of household head, household composition, occupational category as well as involvement social financial safety net programs. CONCLUSION: Findings from this study can be suggestive for policy-makers interested in scaling up health insurance for informal workers in Bangladesh. Shared funding from this large informal sector can generate new resources for healthcare, which is in line with the healthcare financing strategy of Bangladesh as well as the recommendation of the World Health Organization for developing social health insurance as part of the path to 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 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 | 0.001 | 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".