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Record W3013077643 · doi:10.35500/jghs.2020.2.e2

Publication trends on adult under-nutrition versus over-nutrition in India between 1961–2016: a bibliometric analysis

2020· article· en· W3013077643 on OpenAlexafffund
Sagar Rohailla, Eric Lentz, Lesley A. Pablo, S. V. Subramanian, Günther Fink, Fahad Razak

Bibliographic record

VenueJournal of Global Health Science · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsGeography

Abstract

fetched live from OpenAlex

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<16.0 kg/m 2 ) and adult overweight (BMI 25-29.9 kg/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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0320.228
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.398
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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