An outline of anemia among adolescent girls in Bangladesh: findings from a cross-sectional study
Bibliographic record
Abstract
Anemia is a significant wide spread public health threat especially among the adolescent girls who are more vulnerable towards low level of hemoglobin particularly of low and middle income countries (LMICs). We investigated the prevalence of anemia among the adolescent girls (10–19 years) in Bangladesh and its socio-demographics distribution. We collected data digitally in ODK platform from a sub-sample of a nationwide cross-sectional survey of 1314 adolescent girls in 2015. Capillary blood hemoglobin level was estimated using HemoCue®; anthropometric measurements through standardized procedure and details socio-demographic information were captured and analyzed. Malnutrition was defined as BMI-for-age Z-score below -2SD (BAZ < −2SD), measured in WHO-AnthroPlus. Univariate analysis followed by multiple logistic regression were performed to examine the association between socio-demographic variables and anemia, while controlling the effect of potential confounding variables. Overall, 51.6% girls were suffering from any form of anemia (non-pregnant-Hb < 12 g/dl; pregnant-Hb < 11 g/dl) while 46% were mildly (non-pregnant-Hb: 10–11.9 g/dl; pregnant-Hb: 10–10.9 g/dl) and 5.4% were moderately (Hb: 7–9.9 g/dl) anemic while only 0.2% were severely anemic. After controlling for relevant covariates in multiple logistic regression model, malnutrition (AOR: 1.42, 95% CI = 1.0–2.10, p -value = 0.083), non-pregnancy (AOR: 6.10, 95% CI = 2.70–13.78, p -value < 0.001), and households with bottom wealth quintile (AOR: 1.54, 95% CI = 1.03–2.30, p -value = 0.037) were identified as significant risk factors of anemia among adolescent girls of Bangladesh. Higher number of adolescent girls are still suffering from anemia in Bangladesh and non-pregnant adolescent girls contributed the most. Immediate, long term and sustainable public health intervention would require to combat the situation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".