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Record W4382983208 · doi:10.3390/healthcare11131912

Reciprocal Effects between Sleep Quality and Life Satisfaction in Older Adults: The Mediating Role of Health Status

2023· article· en· W4382983208 on OpenAlexaff
Change Zhu, Lülin Zhou, Xinjie Zhang, Christine A. Walsh

Bibliographic record

VenueHealthcare · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsQuality of life (healthcare)Life satisfactionSleep (system call)GerontologyPsychologyMedicineReciprocalDemographySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: to examine the causal relationship between sleep quality and life satisfaction and explore the mediating role of health status on the relationship between sleep quality and life satisfaction. METHODS: A total of 1856 older Chinese people participating in 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included. A cross-lagged panel analysis (CLPA) combined with mediator analysis was utilized. RESULTS: The average sleep quality levels for the years 2011, 2014, and 2018 were 3.70, 3.63, and 3.47 out of 5, respectively. The corresponding average levels of health status were 3.47, 3.44, and 3.39 out of 5, and the average levels of life satisfaction were 3.75, 3.86, and 3.87 out of 5, respectively. In addition, sleep quality at prior assessment points was significantly associated with life quality at subsequent assessments, and vice versa. Also, health status partially mediated this prospective reciprocal relationship. CONCLUSIONS: There is a nonlinear decreased trend in sleep quality and health status, while there exists a nonlinear increased trend in life satisfaction for older adults from 2011 to 2018. Reciprocal positive effects between sleep quality and life satisfaction in older adults exist and are mediated by health status.

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 score0.299
Threshold uncertainty score0.961

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 head0.074
GPT teacher head0.451
Teacher spread0.377 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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