Establishment of Aisyiah Stunting Cadres (KSA) with Tuina Massage Expertise in Buluspesantren Sub-district
Bibliographic record
Abstract
Nutritional problems can cause stunting in children under five which eventually affects the quality of human resources in Indonesia. Stunting is a growth and development disorder caused by poor nutrition, repeated infections, and inadequate psychosocial stimulation in children. Stunting cases reach 149.2 (22%) million children worldwide and 6.3 million children in Indonesia. Stunting is caused by a lack of knowledge, appetite, and poor feeding behavior. Tuina massage is a complementary therapy to increase appetite. This community service aims to establish Aisyiah Stunting Cadres (KSA) with Tuina massage expertise. KSA assists families with stunted children by providing Tuina massage training for mothers and distributing appetite stimulants. This activity covered 3 stages, namely KSA establishment, training, and family assistance. This involved 20 cadres who meet the criteria of women aged 20-50 years who are physically and mentally healthy. The training materials covered stunting, physical examinations, and Tuina massage. The trained cadres had to assist families and distribute appetite stimulants. This community service managed to establish KSA consisting of 20 cadres. The cadre’s knowledge and skills increased after the training. KSA with insufficient knowledge and skills reduced from 30% to 5%, while KSA with sufficient knowledge and skills reduced from 60% to 30%. KSA With good knowledge and skills increased from 10% to 65%. Around 24 children received appetite stimulants. Conclusion: The establishment of KSA and the provision of appetite stimulants can be an alternative solution to address stunting in children under five.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".