If They Get Sick, They are in Trouble: Health Care Restructuring, User Charges, and Equity in Vietnam
Bibliographic record
Abstract
The transition from a centrally planned economy in the 1980s and the implementation of a series of neoliberal health policy reform measures in 1989 affected the delivery and financing of Vietnam's health care services. More specifically, legalization of private medical practice, liberalization of the pharmaceutical industry, and introduction of user charges at public health facilities have effectively transformed Vietnam's near universal, publicly funded and provided health services into a highly unregulated private-public mix system, with serious consequences for Vietnam's health system. Using Vietnam's most recent household survey data and published facility-based data, this article examines some of the problems faced by Vietnam's health sector, with particular reference to efficiency, access, and equity. The data reveal four important findings: self-treatment is the dominant mode of treatment for both the poor and nonpoor; there is little or no regulation to protect patients from financial abuse by private medical providers, pharmacies, and drug vendors; in the face of a dwindling share of the state health budget in public hospital revenues and low salaries, hospitals increasingly rely on user charges and insurance premiums to finance services, including generous staff bonuses; and health care costs, especially hospital costs, are substantial for many low- and middle-income households.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".