Changing trends in nutritional status of adolescent females: a cross-sectional study from urban and rural Bangladesh
Bibliographic record
Abstract
OBJECTIVES: Malnutrition remains a major problem among adolescents worldwide, but the types of nutritional problem impacting this group are changing significantly. This study aims to describe and analyse the trends in nutritional status and related epidemiological characteristics of 10 to 19 years old adolescent girls over time (2001 to 2018) in Bangladesh. METHODS: test for trend to test for statistical significance of the changing trends of undernutrition and overnutrition among the study participants. Multivariable logistic regression model was fit to measure the strength of association, reported as adjusted OR (aOR) and corresponding 95% CIs. RESULTS: A total of 1224 and 628 adolescent females attended urban and rural treatment facilities, respectively, between 2001 and 2018. The proportion of stunting and thinness decreased substantially, whereas overweight has been observed to increase (1.7% to 7.4%, p<0.001) over the study period. Factors independently associated with stunting were illiteracy (aOR 2.39, 95% CI (1.68 to 3.39), p<0.001), monthly family income of less than US$100 (aOR 1.54, 95% CI (1.25 to 1.9), p<0.001) and family belonging to poorest wealth quintile (aOR 1.45, 95% CI (1.13 to 1.87), p=0.004). Younger participants (aOR 2.69, 95% CI (2.10 to 3.45), p<0.001), rural participants (aOR 1.71 95% CI (1.23 to 2.38), p<0.001), longer hospital stay (aOR 1.52, 95% CI (1.16 to 1.98), p=0.002) and monthly family income less than US$100 (aOR 1.44, 95% CI (1.09 to 1.89), p=0.009) were significantly associated with thinness. Conversely, overweight/obesity was associated with monthly family income more than US$100, duration of diarrhoea and rural participants. CONCLUSION: Undernutrition in adolescent girls has decreased with time, but the burden of overweight has increased. Higher literacy and better wealth status were found to be associated with the improved nutritional status of the participants.
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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 | 0.001 |
| 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".