Publication trends on adult under-nutrition versus over-nutrition in India between 1961–2016: a bibliometric analysis
Bibliographic record
Abstract
Background: Despite the nearly 10-fold greater prevalence of underweight versus obesity in India, there is a strong focus on obesity and overweight in policy and media coverage in India.Our objective was to examine the ratio of research articles published on underweight vs overweight in India. Methods:We conducted a bibliographic analysis of peer-reviewed PubMed listed publications on adult underweight (BMI<18.5 kg/m 2 ) /severe chronic energy deficiency (SCED -BMI<16.0kg/m 2 ) and adult overweight (BMI 25-29.9kg/m 2 )/obesity(BMI>30 kg/m 2 ) between the years 1961-2010.Articles were categorized by two reviewers into one of three categories: 1) focused on underweight/SCED, 2) overweight/obesity or 3) both underweight/SCED and overweight/ obesity.We quantified the number of articles in 5-year publication intervals and calculated the ratio between article types in each 5-year intervals.Results: Our search strategy yielded 4099 articles eligible for review, from these a total of 1124 articles were on overweight/obesity, 247 articles on underweight/SCED and 161 articles on both underweight/SCED and overweight/obesity.The inter-coder Cohen Kappa for categorization of articles was 0.92 (standard error, 0.03; 95% confidence interval, 0.86-0.97).From 1996 onwards, there was an increased ratio of overweight/obesity related articles compared to articles on underweight/SCED from 2.7:1 between the years 1996-2000 which rose to a ratio 5:1 after 2010.Conclusion: There is an increasing and disproportionate focus of scientific research on overweight/obesity versus under-nutrition over the past 40 years in India.While obesity requires attention, undernutrition and low body weight continue to be India's most pressing public health and are relatively under-studied in the research literature.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.166 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".