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Record W2044274534 · doi:10.12927/whp.2007.19527

Age-Specific Analysis of Reported Morbidity in Kerala, India

2007· article· en· W2044274534 on OpenAlexvenueno aff
T. R. Dilip

Bibliographic record

VenueWorld health & population · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCasteDisadvantagedDemographyIndian subcontinentMedicineProxy (statistics)Public healthDiseaseGerontologyEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

This paper attempts to provide a wider understanding of the differentials in reported health status in Kerala, while comparing morbidity in the state with other regions in the Indian subcontinent. Reported morbidity and the duration of life lived with a disease is higher in Kerala. Economic inequalities were found only in late-working ages and the elderly, primarily due to higher prevalence of life style-associated chronic conditions in these two age groups. Significant caste-wise differences among adolescents and prime working ages indicated potential for health problems induced by income deprivation in socially disadvantaged subgroups. Self-reported morbidity was 65% higher than proxy-reported morbidity. Regional differences were significant across all age groups, with high morbidity in the most developed region in the state. Results also suggested the need to factor for self- and proxy-reported status in any analysis of morbidity using similar survey data.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.485
Teacher spread0.371 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2007
Admission routes1
Has abstractyes

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