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Record W2913582005 · doi:10.1080/13607863.2019.1571016

Widowhood and depression: a longitudinal study of older persons in rural China

2019· article· en· W2913582005 on OpenAlexafffund
Jie Xu, Zheng Wu, Christoph M. Schimmele, Shuzhuo Li

Bibliographic record

VenueAging & Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsStatistics CanadaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsSpouseGeeDepression (economics)Social supportLongitudinal studyPsychologyGeneralized estimating equationLongitudinal dataSocial isolationChinaStructural equation modelingDepressive symptomsDemographyDevelopmental psychologyGerontologyClinical psychologyPsychiatryMedicineSocial psychologyCognitionSociologyHistory

Abstract

fetched live from OpenAlex

Using six waves of longitudinal data (2001-2015) collected in Anhui, China (N = 2,131) and generalized estimating equations (GEE) models, this study fulfilled several objectives. First, the study compared the widowed to the married to examine if the transition to and duration of widowhood contributes to changes in depression. Second, the study examined if the bereavement-depression relationship is a process that precedes widowhood or is an abrupt change following the death of a spouse. Third, the study examined if social resources influence the bereavement-depression relationship. The study found that there is pre-widowhood effect on depression and that the widowhood event also contributes to increases in depression. Levels of post-widowhood depressive symptoms peak during the first six months bereavement and taper off within 25 months. Controlling for social support, contact with children, and living arrangements does not change the bereavement-depression relationship. The findings support Attachment Theory, which suggests that the loss of a spouse leads to emotional isolation that cannot be overcome with kin-based social support and social integration.

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)0.000
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 score0.012
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.370
Teacher spread0.346 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

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