Interdisciplinary Brain-based Learning Strategies in Addressing Liver Fluke Disease and Cultural Dietary Practices among Early Childhood Students
Bibliographic record
Abstract
Liver fluke disease remains a critical public health issue in Southeast Asia, driven by cultural dietary practices involving raw or undercooked freshwater fish. This study evaluates the impact of an interdisciplinary brain-based learning intervention aimed at increasing awareness and modifying dietary behaviors among early childhood students in Central Northeast Thailand. A quasi-experimental design was employed, involving 122 students from four provinces. The intervention incorporated brain-based learning activities such as storytelling, songs, and visual aids to enhance engagement. Pre- and post-tests, behavioral observations, and attitudinal surveys were conducted to assess the program’s effectiveness. Post-intervention results indicated significant improvements in knowledge about liver fluke transmission, symptoms, and prevention, alongside positive changes in behavior, such as increased avoidance of raw fish consumption and improved hygiene practices. Attitudinal surveys showed a shift in preferences toward safer eating habits. These findings suggest integrating brain-based learning with culturally relevant health education can foster sustainable behavior change in early childhood populations. Further research is recommended to explore this approach’s long-term impacts and scalability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".