Food insecurity and nutritional status of preconception women in a rural population of North Karnataka, India
Bibliographic record
Abstract
BACKGROUND: As per the World Health Organization, the nutritional status of women of reproductive age is important, as effects of undernutrition are propagated to future generations. More than one-third of Indian women in the reproductive age group are in a state of chronic nutritional deficiency during the preconception period leading to poor health and likely resulting in low birth weight babies. This study was aimed to assess the food insecurity and nutritional status of preconception women in a rural population of north Karnataka. METHODS: A total of 770 preconception women were enrolled across a district in Karnataka from selected primary health centre areas by a cluster sampling method. Data on socioeconomic status, food insecurity and obstetric history were collected by trained research assistants, interviewing women at home. In half of the participants, a 1 day 24 -hour dietary recalls were conducted by dietary assistants to assess the dietary intakes. Anthropometric measurements and haemoglobin estimation were carried out at the health centres. RESULTS: In the present study, a majority of the participants (64.8%) belonged to the lower socio-economic classes and the prevalence of food insecurity was 27.4%. A majority of the participants had mild (15.5%) to moderate (78.6%) anaemia. About one-third of the participants (36.6%) were underweight. Significant associations were found between socio-economic status and anaemia (p = 0.0006) and between food insecurity and anaemia (p = 0.0001). CONCLUSION: The nutritional status of preconception women was poor and anemia was more prevalent in low-socioeconomic and food insecure population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".