Who Receives Healthcare? Age and Sex Differentials in Adult Use of Healthcare Services in Rural Bangladesh
Bibliographic record
Abstract
Use of healthcare services may vary according to the cultural, social, economic and demographic situation of the person who may need care. In certain contexts, it particularly varies with age and sex of the potential user. Bangladesh is a less developed, primarily rural and predominantly Muslim traditional society with a pluralistic healthcare system. This paper endeavours to delineate the age, sex and other factors associated with obtaining healthcare in this pluralistic system. Using the Matlab Health and Socio-economic Survey, the paper uses logistic regression to ask whether factors commonly related to Western healthcare utilization in a theoretical framework useful in the study of Western research on healthcare services are also useful in the study of healthcare utilization in the developing world. Elderly women, never-married women and Hindus were less likely to visit any practitioner, which may indicate less health empowerment for these groups. Obtaining care is inversely related to household size and positively related to age (for men), education, poor health status and impaired mobility. Controlling for these factors, household wealth and ever-married status showed no significant effect on obtaining care. The differential in use of healthcare services can partially be ameliorated by changes in policy related to the elderly and women.
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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 | 0.000 | 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".