Prevalence and Determinants of Anemia among Adolescent Girls: A School-Based Survey in Central Java, Indonesia
Bibliographic record
Abstract
INTRODUCTION: Anemia is the most common and inflexible nutritional problem affecting about 2 billion of the world’s population with a significant impact on human health and social and economic development. Information about anemia prevalence and associated factors among adolescent girls in Indonesia is still limited. OBJECTIVE: This study aimed to examine determinant factors related to anemia among adolescent girls. METHODE: This is an analytic study with a cross-sectional design, located in three regencies in Central Java Indonesia. This is a school-based survey in several senior high schools in three regencies that have a higher number of stunting cases, as a related indicator of Anemia. A total of 388 adolescent girls have participated in this study. Anemic status was assessed using HB quick-check. Independent variables such as breakfast habit, father’s height, allowance per day, etc. were collected by a structured questionnaire. Data analysis is carried out by univariate, bivariate, and multivariate. Ethical clearance has been approved by Medicine Faculty Ethics Committee, Jenderal Soedirman University. RESULT: The study found that 26,3% of girls were categorized as having anemia. Results showed that there was a correlation between anemic and breakfast habit (p = 0.07), and allowance per day (p = 0.08), and father’s height (p = 0.01) among adolescent girls. CONCLUSION: This research highlighted the importance for the adolescent girl of having daily breakfast. Good eating habits can help to reduce the incidence of anemia and its negative adverse health effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".