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Record W1988082409 · doi:10.4103/0019-5278.16035

An overview of occupational health research in India

2005· article· en· W1988082409 on OpenAlexaff
Ramanakumar V. Agnihotram

Bibliographic record

VenueIndian Journal of Occupational and Environmental Medicine · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOccupational safety and healthAgricultureBusinessLegislationOccupational hygieneIndustrialisationEnvironmental healthEnforcementEconomic growthOccupational health nursingMedicinePolitical scienceHealth careHealth policyGeographyEconomics

Abstract

fetched live from OpenAlex

Recent industrialization and globalizations are changing the Indian occupational morbidity drastically. Traditionally labor-oriented markets are on change towards more automation and mechanization, at the same time general awareness about occupational safety, occupational and environmental hazards were not spread in the society. This review will provide an overview of existing evidence from community based epidemiological studies and address the growing needs for evidence-based occupational health research in India. Review of all published results. Occupational research is seen as more complex issue in India, which Includes child labor; poor industrial legislation; vast informal sector; less attention to industrial hygiene and poor surveillance data across the country. While India experiencing economic transition, occupational research approach should balance between understanding the modern industrial exposures and health risks of traditional sectors like agriculture and plantations. Strategies like modern occupational health legislation, enforcement machinery in sub-district level, training to health professionals, need for epidemiological evidence and international collaborations were discussed to deal with the situation.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.076
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.411
Teacher spread0.260 · 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 teacher head, not a consensus.

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

Citations50
Published2005
Admission routes1
Has abstractyes

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