Bibliographic record
Abstract
The prevalence of anemia in pregancy in Indonesia is 40.1 % by National Family Health Survey 2001. Data from Health Office Pekanbaru City showed prevalence of anemia in pregnancy in Tenayan Raya Public Health Center is 54 %. This cross sectional study was designed to identify risk factors with anemia in pregnancy in Tenayan Raya Public Health Center. Two hundreds and twelve pregnant women were included in this study . Data obtained usinh questionnaire and analyzed using regression logistic test. This study showed Parity (CI95%: OR = 1.66 to 6.16), adequacy of iron intake (CI95%: OR = 1.59 to 5.80), Status KEK (CI95%: OR = 1.44 to 2.50), maternal education (CI95%: OR = 1.24 to 4.50) were significantly associated with incidence of anemia in pregnancy. Unrelated variables are maternal age, family income, employment and knowledge. multivariate analysis showed the parity (number of children), the adequacy of iron intake, KEK status, and maternal education have a causal relationship with the incidence of anemia. Variables that are not associated significantly with the incidence of anemia in pregnant women is the mother's age, family income, occupation and knowledge. Suggest for pregnant women to plan their pregnancy, consume iron tablets as many as 30 eggs / month for 3 consecutive months and maintain the nutritional needs during pregnancy. For further research needs to study with different respondents, a different place and time by using continuous data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".