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Record W2301855657 · doi:10.1016/j.ssmph.2016.02.006

Improving but unequal: Temporal trends in Chinese self-rated health, 1990–2012

2016· article· en· W2301855657 on OpenAlexaff
Soyoung Kwon, Markus H. Schafer

Bibliographic record

VenueSSM - Population Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
FundersNational Institute on Minority Health and Health Disparities
KeywordsCohort effectDemographyCohortChinaEducational attainmentCohort studyCovariateMedicinePsychologyGerontologyGeographyEconomicsEconometricsEconomic growthSociology

Abstract

fetched live from OpenAlex

This study examines temporal trends in the self-rated health of Chinese adults from 1990 to 2012. Concentration on this particular period in Chinese history provides insights into the health implications of China's massive societal transformation induced by economic reform. A series of cross-classified random effects models were estimated predicting favorable health status across time periods and adjusted for age, cohort effect and individual-level covariates. Results show that more recent birth cohorts exhibit better health conditions than earlier birth cohorts. However, period effects had a more profound effect than that of birth cohort. Net of age, cohort and individual-level covariates, there is a significant and increasing trend in self-rated health since the early 1990s. The period pattern was non-monotonic, with health improvement in the early 1990s, a dip later in that decade, but more evidence of improvement by 2012. We also found that health disparities have widened over the past 20 years, particularly on the basis of income and educational attainment.

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 score0.141
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.393
Teacher spread0.356 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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