Taking account of context: how important are household characteristics in explaining adult health-seeking behaviour? The case of Vietnam
Bibliographic record
Abstract
Understanding the factors affecting the utilization of health services is essential for health planners, especially in low income countries where increasing access to and use of health services is one of the main policy goals of government. While much has been written on adult health-seeking behaviour, there is comparatively little known about the influence of the broader context such as the effects of family and community on individual use of health care services in low income countries. Using Vietnam's latest National Household Survey data, this paper empirically assesses the influence of individual- and household-level factors on the use of health care services, while controlling for the unobserved household-level effects. The estimates obtained from a multilevel logistic regression model suggest that the individual's likelihood of seeking treatment is jointly determined by the observed individual- and household-level characteristics as well as unobserved household-level effects. The chance of seeking medical treatment when ill varies strongly with the observed individual- and household-level covariates, including health insurance status, income, the type and severity of illness, the number of other household members with an ailment and the presence of young children in the household. However, the variability implied by the unobservable household-level effects outweighs the variability implied by the observed covariates, indicating a high degree of homogeneity in health-seeking behaviour among the household members. Failure to take account of homogeneity in health-seeking behaviour among the household members leads not only to biased results but also to inefficient policy targeting. Policies aimed at increasing access to and the use of medical services need to be sympathetic to both individuals and households.
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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 | 0.001 | 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".