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Record W4392139051 · doi:10.1016/j.ssmph.2024.101645

Intergenerational reproduction and self-assessed mental health in adulthood in China

2024· article· en· W4392139051 on OpenAlexaff
Xueqing Zhang, Gerry Veenstra

Bibliographic record

VenueSSM - Population Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocioeconomic statusMental healthRespondentEducational attainmentSocial classDemographyPsychologyGerontologyMedicinePopulationPsychiatrySociologyEconomic growth

Abstract

fetched live from OpenAlex

Physical and mental health disparities by socioeconomic status in China are well documented but the effects of the intergenerational reproduction in socioeconomic status on adult mental health have received little attention to date. We utilized cross-sectional data from the 2017 Chinese General Social Survey to examine the significance of intergenerational socioeconomic reproduction for differences in self-assessed mental health in a national sample of Chinese adults between the ages of 23 and 65. We documented substantial elasticities between the socioeconomic status of the survey respondents and their parents: father's education, mother's education and childhood social class were all associated with both respondent education and respondent household income. We also found that associations between parental socioeconomic status and their adult children's self-assessed mental health were partly explained by the children's own socioeconomic status. However, these pathways were noticeably moderated by age cohort. Among younger people, associations between parental socioeconomic status and mental health were mostly explained by educational attainment whereas among older people associations between parental socioeconomic status and mental health were mostly explained by household income. In general, parental socioeconomic status appear to have a greater influence on the mental health of people who grew up after the Chinese economic reform of the 1970s.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.923

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 studies0.0000.000
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 head0.021
GPT teacher head0.382
Teacher spread0.362 · 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
Published2024
Admission routes1
Has abstractyes

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