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Record W2364014910 · doi:10.1080/13607863.2016.1181708

Worry about not having a caregiver and depressive symptoms among widowed older adults in China: the role of family support

2016· article· en· W2364014910 on OpenAlexaff
Ling Xu, Yawen Li, Joohong Min, Iris Chi

Bibliographic record

VenueAging & Mental Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWorryModerationPsychologyDepressive symptomsCoping (psychology)FeelingClinical psychologyCaregiver stressCaregiver burdenGerontologyAnxietyPsychiatryMedicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVES: Using the stress-coping framework, this study examined whether worry about not having a caregiver in old age was associated with depressive symptoms among widowed Chinese older adults, including the moderating effects of self-perceived family support. METHOD: Using a sample of 5331 widowed adults aged 60 years old or older from the 2006 National Sample Survey of the Aged Population in Urban/Rural China, we regressed measures of depressive symptoms on worry about not having a caregiver. We also tested moderation effects of family support. RESULTS: Individuals who were worried about not having a caregiver reported significantly higher levels of depressive symptoms. Feeling that their children are filial, having instrumental support from children, and having only daughters moderated the effects of worry about not having a caregiver on depressive symptoms. CONCLUSION: Our findings indicate the detrimental effects of worry about not having a caregiver on the psychological well-being of widowed older adults. This study also highlights some forms of family support that may help reduce such negative effects of widowhood.

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 score0.254
Threshold uncertainty score0.982

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 head0.004
GPT teacher head0.266
Teacher spread0.262 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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