Optimizing the mix of basic dental services for Southern Thai schoolchildren based on resource consumption, service needs and parental preference
Bibliographic record
Abstract
OBJECTIVES: To identify the optimal levels and mix of basic dental services (sealants and fillings for permanent teeth and extraction of primary teeth) under two different dental settings: hospital-based and mobile dental clinics under specified resource constraints. METHODS: A linear programming model is used based on explicit identification of system objectives and resource constraints. The objective was to maximize benefits as measured by parental willingness to pay (WTP) for basic dental services provided to schoolchildren subject to constraints on total resources, service need and parental preferences among different dental care settings. RESULTS: Optimization was identified to require 270, 180, 552, 828, 228 and 532 cases of hospital sealant, mobile sealant, hospital filling, mobile filling, hospital extraction and mobile extraction, respectively. The corresponding current service levels were 48, 281, 191, 170, 479, and 677 respectively. The optimal service configuration produced a total WTP of 485 860 baht which exceeded the WTP for the current service configuration by more than 75.4%. CONCLUSIONS: Mobile clinic fillings were the highest priority among basic dental services. The current service configurations fail to reflect the setting preferences and provide greater emphasis to extractions than the optimal configuration with less emphasis given to preventive and restorative services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".