The Analysis of Anemia Prevention Model in Pregnant Women in Banten
Bibliographic record
Abstract
Anemia among pregnant women, especially in developing countries is still becoming problematic. Although several programs implemented, they showed a modest impact on the reduction. It is essential to develop the efficient approach for tackling this problem. This study was aimed to identify and develop the model of anemia associated modality care that can be used for preventing and managing of anemia during pregnancy. This research was a cross-sectional study of creating a prevention model using Structural Equation Modeling (SEM PLS) technique. This technique was to find out which indicator variables has the direct and indirect influence of causing anemic pregnant women. This research was conducted in the Kaduhejo, Pandeglang, Banten in 2018 and involved 258 pregnant women living with their families. These respondents were recruited using multistage cluster sampling. Data collection was conducted by a questionnaire to identify the pregnant women characteristics and maternal knowledge, attitudes, perceptions, and family support. The models were constructed to arrange the intervention module as well as analyzing model using SEM-PLS. The results of this study showed that exogenous variables had a statistically significant T value reflected on the variable> 1.96, thus indicating that the indicator block had a positive and significant effect of reflecting the variable. In conclusion, anemia among pregnant women influenced by direct factors, such as family support, maternal knowledge and perception.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".