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Record W4403129167 · doi:10.1177/08982643241289516

Number of Chronic Conditions and Death Anxiety Among Older Adults in Rural China: A Longitudinal Study in Anhui Province

2024· article· en· W4403129167 on OpenAlexaff
Jin Guo, Andrew Wister, Barbara S. Mitchell, Shuzhuo Li

Bibliographic record

VenueJournal of Aging and Health · 2024
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsSimon Fraser University
FundersNational Natural Science Foundation of China
KeywordsWorryAnxietyDeath anxietyLongitudinal studyFeelingMedicineDemographyChinaGerontologyPsychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Objectives: Death anxiety is feelings of worry and fear regarding death. This study explored the effect of number of chronic conditions on death anxiety in older adults and the moderating effect of age. Methods: This study used the fifth, sixth, seventh, and eighth waves of longitudinal data (2012–2021) collected in Anhui, China (5014 person-year observations). A mixed linear model was used to examine the effect of number of chronic conditions on death anxiety and the moderating effect of age. Results: The number of chronic conditions showed an inverted U-shaped relationship with death anxiety, with death anxiety being lower in older adults with older age. As age increased, the U-shaped curve became flatter, and the extremes shifted to the right. Discussion: Particular attention should be given to younger older adults with chronic conditions to help them recover earlier from the negative impact by providing information and counseling about their chronic conditions.

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)0.001
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 score0.023
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.378
Teacher spread0.358 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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