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Record W2550241561 · doi:10.1177/1054137316678549

Epidemiological Transition and Population Health: Understanding Social Determinants of Health in China

2016· article· en· W2550241561 on OpenAlexafffund
Soma Hewa, Bo Liu

Bibliographic record

VenueIllness Crisis & Loss · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsQueen's University
FundersUniversity of British Columbia
KeywordsEpidemiologyEpidemiological transitionHealth promotionSocial determinants of healthContext (archaeology)Public healthDiseasePopulation healthPopulationChinaSocial epidemiologyMedicineEnvironmental healthGlobal healthEconomic growthPolitical scienceGeographyPathology

Abstract

fetched live from OpenAlex

This article has twin objectives: First, the article briefly examines major theoretical interpretations of disease causations in Western medicine, their limitations in understanding social epidemiology, and the gradual development of the population health approach to health promotion and disease prevention in the context of chronic diseases in Western industrialized societies. Second, the article examines the current epidemiological trends in China and the relevance of population health perspectives and strategies to promote health. While analyzing some recent findings on social determinants of health in China, the article argues that effective population health strategies for health promotion must be based on a social epidemiology that provides information necessary to promote health. Although infectious diseases still make a significant contribution to China’s mortality and morbidity figures, the incidence of chronic diseases such as malignancies, heart disease, respiratory disease, and cerebrovascular disease is steadily increasing. Finally, in view of the current epidemiological trend, and the need to tackle the multiple health challenges, this discursive analysis proposes a number of key research areas within the broader context of social epidemiology that may facilitate future health policies in China.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.100
GPT teacher head0.411
Teacher spread0.311 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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