Evidence map›Paper›PMID 38249402›Full record

ArticleFrontiers in public health2023

Subjective well-being patterns in older men and women without someone to confide in: a latent class analysis approach.

Dijuan Meng, Chang Sun

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Dijuan MengSchool of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.
Chang SunSchool of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.
Nanjing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to identify the latent subtypes of subjective well-being (SWB) and associated factors in older adults without a confidant in China. Methods: The data came from the most recent (seventh) wave (2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). This cross-sectional study included 350 older adults who lacked a close confidant. We utilized latent class analysis and multiple logistic regression models to examine the latent SWB subtypes and associated factors. Results: Three distinct patterns of SWB were identified: the very low SWB class (32%), the medium-low SWB class (46%), and the low evaluative and high affective SWB class (22%). The results indicated that compared to the low evaluative and high affective SWB class, respondents who self-rated their health as not good, currently drank alcohol and rated their financial status as poor/very poor were more likely to be in the very low SWB class, while those who participated in social activities were less likely to be in the very low SWB class. Respondents who had limitations in instrumental activity of daily living (IADL) and rated their financial status as poor/very poor were more likely to be in the medium-low SWB class. However, gender did not affect SWB patterns. Conclusion: Our findings highlight awareness of the heterogeneity of SWB in older adults without close confidants and provide valuable information for the development of tailored intervention programs to improve their well-being.

Indexed as

Asian PeopleInterpersonal RelationsSocial Determinants of HealthSocial IsolationAgedChinaCross-Sectional StudiesEthanolFemaleHumansLatent Class AnalysisMaleEthanolCLHLSconfidantslatent class analysisolder adultssubjective well-being

Identifiers

PMID38249402
PMCPMC10796680
OpenAlexW4390610392

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.