INTERVENSI GIZI SEBAGAI UPAYA PENCEGAHAN ANEMIA PADA REMAJA PUTRI DI WILAYAH KERJA PUSKESMAS PEKAYON JAYA KOTA BEKASI
Bibliographic record
Abstract
Introduction: Overcoming nutritional problems around the Pekayon Jaya Community Health Center. Based on these problems, anemia in adolescent girls is the highest problem at the Pekayon Jaya Community Health Center. This activity was carried out in the Gema Karya Bahana High School, Bekasi City, because based on e-PPGBM data, the prevalence of cases of anemia in teenagers, especially young women, in 2023, the Gema Karya Bahana Vocational School area, Bekasi City, has the highest anemia problem with a prevalence of 64.6%. This intervention activity aims to increase the active role of young women in preventing and overcoming the health problem of anemia. Method: This community service activity was carried out at Gema Karya Bahana Vocational School, Bekasi City. The method used was consecutive sampling, namely the sample was selected according to research criteria, where the target of this activity was 25 young women in grades 10 and 12 at SMK Gema Karya Bahana, Bekasi City. Secondary data was obtained through web access for Community Based Nutrition Recording and Reporting (e-PPGBM). Meanwhile, primary data was obtained based on the results of direct field surveys. Research Result: Based on the results of the counseling and data processing that has been carried out, the results show that the correlation coefficient (Correlation) value is 0.522 with a significance value (Sig.) of 0.007. Conclusion: It can be concluded that the data is normally distributed, so it can be said that there is a relationship between the pre-test variables and the post-test variables.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".