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Record W4412099314 · doi:10.3389/fpsyg.2025.1606373

The relationship between spousal empowerment, quality of life, and subjective wellbeing among disabled elderly

2025· article· en· W4412099314 on OpenAlexaboutno aff
Fei Ye, Yuanrong Wu, Chao‐Yang Pan, Zifen An, Yanzhen Zhai, Dan Wang, Liping Yu, Yanni Zhu

Bibliographic record

VenueFrontiers in Psychology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersSouthern Medical University
KeywordsPsychologyEmpowermentQuality of life (healthcare)Well-beingGerontologyDevelopmental psychologyPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Background: As rapidly ages, the number of disabled elderly is increasing, leading to lower quality of life and greater psychological stress. Aim: To explore the relationship between spousal empowerment, quality of life, and subjective wellbeing (SWB) among disabled elderly, providing insights and practical guidance for enhancing SWB in this demographic. Method: A convenience sampling approach was employed to select 332 disabled elderly and their spouses. Research tools included a demographic survey, the Barthel Index (BI), the World Health Organization Quality of Life Assessment - Older Adults Version (WHOQOL-OLD), the Memorial University of Newfoundland Scale of Happiness (MUNSH), and the Main Caregivers' Empowerment Measurement (MCEM). Statistical analysis was performed using SPSS and AMOS. Result: = 0.032) as significant predictors of SWB. The results of testing the mediating role of spousal empowerment using the structural equation model show that quality of life directly predicted SWB with a path coefficient of 0.208 (95% CI: 0.065, 0.289). Spousal empowerment partially mediated the relationship between quality of life and SWB, with a mediation effect of 0.067 (95% CI: 0.026, 0.098). Conclusion: Both quality of life and spousal empowerment can positively influence the SWB of disabled elderly. Additionally, spousal empowerment partially mediates the relationship between quality of life and SWB.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.085
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.472
Teacher spread0.391 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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