Evidence map›Paper›PMID 33171696›Full record

ArticleInternational journal of environmental research and public health2020

Identification, Trend Analysis and Influencing Factors of Mental Health Status of the Chinese Older Adults.

Hongyan Yang, Jun Ma, Hongwei Hu, Fanjie Li

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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

12 citing papers in PubMed.

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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

4 authors.

Hongyan YangCenter for Social Security Studies, Wuhan University, Wuhan 430072, China.
Jun MaCenter for Social Security Studies, Wuhan University, Wuhan 430072, China.
Hongwei HuSchool of Public Administration and Policy, Renmin University of China, Haidian District, Beijing 100872, China.
Fanjie LiInternational Education School of Zhongnan University of Economics and Law, Wuhan 430073, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to analyse the classification, development trends and the influencing factors of the Chinese older adults' mental health state. Based on longitudinal data of Chinese older adults from 2005 to 2014, 2077 older adults aged 64 to 105 were included and the Latent Class Model, Latent Growth Mixture Model and Multinomial Logit models were employed in this study. We find that there are three types of mental health state of the Chinese older adults: negative, positive and contradictory; and the contradictory type could easily turn into negative or positive mental health state. There are four types of dynamic trends of mental health state: persistently negative, persistently positive, pro-negative, and pro-positive. About 40% of the older adults could maintain positive mental health state, and the pro-negative accounts for larger proportion than the pro-positive. Better economic status, good living habits, cohabitation with family members and pension coverage are beneficial for positive mental health state of the Chinese older adults. There is significant heterogeneity in the state as well as development trends of mental health of the older adults. The older adults with contradictory and negative types of mental health state should get timely psychological help to avoid turning into negative state. A series of polices are needed to promote mental health for the older adults in China.

Indexed as

AgedAged, 80 and overAsian PeopleChinaFemaleHealth StatusHumansMaleMental HealthMiddle AgedSocioeconomic Factorsinfluencing factorslatent categorial analysismental health statetrend

Identifiers

PMID33171696
PMCPMC7664866

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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.