Comparative quality of private and public health services in rural Vietnam
Bibliographic record
Abstract
BACKGROUND: Private health care services were officially recognized in Vietnam in 1989, and for the last 15 years have competed with the public health system in providing primary curative care and pharmaceutical sales to rural populations. However, the quality of these private and public health care services has not been evaluated and compared. METHODS: A community-based survey was conducted in 30 of the 160 communes in Hung Yen, which were selected by probability proportional to population size (PPS) sampling. All commune health centres (CHCs) and private health care providers in the selected communes were surveyed on human resources, services provided, availability of medical equipment and pharmaceuticals, knowledge and clinical performance for acute and chronic problems. Patient satisfaction and cost of care associated with recent illness were measured using a random household survey covering 30 households from each of the selected communes. RESULTS: There were 11.5 private providers per 10,000 population, compared with 6.7 public providers per 10,000. A quarter of private providers were employees of the public health sector. Less than 20% of the private providers had registered their practice with the government system. Eleven per cent (26/234) had no professional qualifications. Fifty-eight per cent (135/234) provided treatment as well as selling medications. Public sector infrastructure was superior to that of the private providers. The quality of services provided by public providers was poor but significantly better than that of private providers. Patient satisfaction and costs of care were similar between the two groups. CONCLUSIONS: Private providers are successfully competing with the public health centre system in rural areas but not because they provide cheaper or better services. The quality of private health care services is not controlled and is significantly poorer than public services. Current practice in both systems falls below the national standard, especially for the management of chronic health problems. The low quality of health care services at a community level may help explain the previously observed phenomena of high levels of self-medicating, low utilization of commune health centres and over-utilization of tertiary health care facilities.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".