UPAYA PENINGKATAN PENGETAHUAN KADER TIM PENDAMPING KELUARGA (TPK) DALAM PERCEPATAN PENURUNAN STUNTING DI KECAMATAN PASAR KOTA JAMBI
Bibliographic record
Abstract
Stunting is the result of the interaction of various factors, namely insufficient food intake and increased nutritional needs. The purpose of this service activity is to improve the performance of the Family Assistance Team (TPK) cadres as the spearhead in efforts to accelerate stunting reduction in Telanaipura sub-district, Jambi City. This activity uses a qualitative approach related to optimizing the performance of the Family Assistance Team (TPK) cadres with counseling in an effort to accelerate stunting reduction. Overall, this intervention can accelerate the reduction of stunting in Pasar Sub-district, Jambi City. Improving the performance of the Family Support Team (TPK) administrators who foster families is expected to have a positive impact on efforts to reduce stunting in the Pasar District of Jambi City. This success can serve as an example for similar efforts in other areas experiencing stunting. The success of this activity shows the importance of careful planning, providing relevant and interactive materials, and effective follow-up. The experience of this activity will serve as a reference for future awareness activities, and is expected to make a major contribution to broader efforts to reduce stunting.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".