Penalizing patients and rewarding providers: user charges and health care utilization in Vietnam
Bibliographic record
Abstract
The introduction of a comprehensive system of user charges in 1995 provided public health facilities in Vietnam, especially hospitals, with a growing source of revenue. By 1998 revenues from user charges accounted for 30% of public hospital revenues. Increasingly, provider incomes have relied on fee revenues and provision-based bonuses, the effect of which is that a poorly regulated fee-for-service system has replaced a salary system based upon a centrally determined global budget. This paper examines the potential influence of providers' on the use of publicly provided health services. Using facility-based data over the period 1996-98, the relative contribution of treatment intensity is compared and contrasted under the two sources of hospital revenues from patients, namely a user charge system and a third party payment system based on fee-for-services. The primary focus of the comparison is on the treatment intensity for all hospital contacts, hospital admissions and the length of hospital stays, decisions normally taken by the providers and over which patients have little or no influence. The results indicate that growth in patient revenues was associated with large increases in intensity. The growth in intensity was more pronounced in the case of inpatient contacts. Moreover, both the admission rate and the length of hospital stay were far higher for better off individuals than for the poor, and greater for the insured than the uninsured. The increase in the intensity of hospital care for both health insurance enrollees and the uninsured can be seen as, among other things, an attempt on the part of providers to increase revenue from health insurance premiums and user charges in the face of a shrinking share of public resources allocated to hospitals, and low wages and salaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".