Health care utilisation under the 30-Baht Scheme among the urban poor in Mitrapap slum, Khon Kaen, Thailand: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In 2001, the Government of Thailand introduced a universal coverage scheme with the aim of ensuring equitable health care access for even the poorest citizens. For a flat user fee of 30 Baht per consultation, or for free for those falling into exemption categories, every scheme participant may access registered health services. The exemption categories include children under 12 years of age, senior citizens aged 60 years and over, the very poor, and volunteer health workers. The functioning of these exemption mechanisms and the effect of the scheme on health service utilisation among the poor is controversial. METHODS: This cross-sectional study investigated the prevalence of 30-Baht Scheme registration and subsequent self-reported health service utilisation among an urban poor population in the Teparuk community within the Mitrapap slum in Khon Kaen city, northeastern Thailand. Furthermore, the effectiveness of the exemption mechanisms in reaching the very poor and the elderly was examined. Factors for users' choice of health facilities were identified. RESULTS: Overall, the proportion of the Teparuk community enrolled with the 30-Baht Scheme was high at 86%, with over one quarter of these exempted from paying the consultation fee. User fee exemption was significantly more frequent among households with an above-poverty-line income (64.7%) compared to those below the poverty line (35.3%), chi2 (df) = 5.251 (1); p-value = 0.018. In addition, one third of respondents over 60 years of age were found to be still paying user fees. Self-reported use of registered medical facilities in case of illness was stated to be predominantly due to the service being available through the scheme, with service quality not a chief consideration. Overall consumer satisfaction was high, especially among those not required to pay the 30 Baht user fee. CONCLUSION: Whilst the 30-Baht Scheme seems to cover most of the poor population of Mitrapap slum in Khon Kaen, the user fee exemption mechanism only works partially with regard to reaching the poorest and exempting senior citizens. Service utilisation and satisfaction are highest amongst those who are fee-exempt. Service quality was not an important factor influencing choice of health facility. Ways should be sought to improve the effectiveness of the current exemption mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".