A study on the prevalence of under-nutrition among the irular tribal adolescent girls in thiruvallur district, tamil nadu, south india.
Bibliographic record
Abstract
Background: Under-nutrition during the adolescence is an important public health problem in developing countries particularly in rural India. There is paucity of data on Under-nutrition among the Tribals wherein early detection prevents adverse health problems Objective: To assess the prevalence of under-nutrition among the Irular adolescent girls in Tamil Nadu. Methods: A community based Cross-sectional study was conducted among 200 Irular girls aged 10 to 19 years in Irularpalayam in Minjur block, Thiruvallur District of Tamil Nadu using the Multi-stage sampling method during March to July 2013. Socio-demographic data, anthropometric measurements, Hemoglobin estimation were recorded and Thinness was defined as BMI < 5th centile (CDC 2000). Anemia was defined as Hb < 12 gm% for non-pregnant girls Results: The prevalence of Thinness was 63.5% with increasing severity with advancing age with statistical significance. Prevalence of Anemia was 58% with increasing severity with age. Majority of 70% had clinical signs of Under-nutrition. Access to Health services was observed to be very low in this community.Conclusion: Under-nutrition in the form of Thinness and Anemia is highly prevalent among the Irular adolescent girls requiring special focus on Health education, nutritional interventions for a healthy productive life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".