Patients’ choice of healthcare providers and predictors of modern healthcare utilisation in Bangladesh: Household Income and Expenditure Survey (HIES) 2016–2017 (BBS)
Bibliographic record
Abstract
OBJECTIVES: The number of modern healthcare providers in Bangladesh has increased and they are well equipped with modern medical instruments and infrastructures. Despite this development, patients seeking treatment from alternative healthcare providers are ongoing. Hence, this study aims to determine the underlying predictors of patients' choosing modern healthcare providers and health facilities for getting treatments. SETTING: Data from the nationally representative Household Income and Expenditure Survey 2016-2017 conducted by the Bangladesh Bureau of Statistics were used. PARTICIPANTS: 34 512 respondents sought treatment for their illnesses from different types of available healthcare providers. PRIMARY AND SECONDARY OUTCOME MEASURE: Patients' choice of healthcare providers (primary) and predictors of patients' choice of modern healthcare providers (secondary). RESULTS: The study found that 40% of the patients visit modern healthcare providers primarily on having symptoms of illness, and the remainder goes to alternative healthcare providers. Patients living in urban areas (adjusted OR (AOR)=1.11, 95% CI 1.05 to 1.17, p<0.01), and if the travel time was between 1 and 2 hours (AOR=1.11, 95% CI 1.00 to 1.22, p<0.05) compared with travel time less than 1 hour, were positively associated to utilisation of modern healthcare facilities for their first consultation. The statistical models show that the predisposing and need factors do not significantly impact patients' choice of modern healthcare providers. CONCLUSIONS: The distribution of modern healthcare providers should be even across the country to eliminate the rural-urban divide in modern healthcare utilisation. Enhancing the digital provision of modern healthcare services could reduce travel time, omit transportation costs and save waiting time for treatment by the modern healthcare providers. Policymakers can think of introducing a national health insurance programme in Bangladesh as a potential policy instrument.
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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 | 0.002 | 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".