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Record W2521617139 · doi:10.1080/01459740.2016.1231676

Decoding the Type 2 Diabetes Epidemic in Rural India

2016· article· en· W2521617139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Anthropology · 2016
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of WinnipegCanadian Mennonite UniversityUniversity of Guelph
FundersCanadian Institutes of Health ResearchInternational Development Research Centre
KeywordsTamilSocioeconomic statusPublic healthEliteDiseaseType 2 diabetesType 2 Diabetes MellitusRural areaGerontologyObesityGeographySocioeconomicsDiabetes mellitusMedicinePoliticsEnvironmental healthEconomic growthSociologyPolitical sciencePopulation

Abstract

fetched live from OpenAlex

Type 2 diabetes mellitus is an escalating public health problem in India, associated with genetic susceptibility, dietary shift, and rapid lifestyle changes. Historically a disease of the urban elite, quantitative studies have recently confirmed rising prevalence rates among marginalized populations in rural India. To analyze the role of cultural and sociopolitical factors in diabetes onset and management, we employed in-depth interviews and focus groups within a rural community of Tamil Nadu. The objectives of the study were to understand sources and extent of health knowledge, diabetes explanatory models, and the impact of illness on individual, social, and familial roles. Several cultural, socioeconomic, and political factors appear to contribute to diabetes in rural regions of India, highlighting the need to address structural inequities and empower individuals to pursue health and well-being on their own terms.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.326
Teacher spread0.310 · 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