Evidence map›Paper›PMID 37427261›Full record

ArticleFrontiers in public health2023

The mental health of rural older adults: the role of the built environment in Jintang County.

Ping Liang, Yan Wang, Tong Wang

Abstract 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 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

3 authors.

Ping LiangHumanities and Law School, Chengdu University of Technology, Chengdu, China.
Yan WangDepartment of Engineering Management, Sichuan College of Architectural Technology, Deyang, China.
Tong WangFaculty of Architecture and Built Environment, Delft University of Technology, Delft, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mental health of older adults has become one of the major health challenges facing society today, which has attracted wide concerns from scholars in urban areas, but research in rural areas has been neglected. Therefore, this paper took rural older adult residents of 11 sample villages in Jintang County, Chengdu City, Sichuan Province, as the research object. After controlling the demographic characteristics of older adults in rural areas, this paper attempted to explore the effects of the rural built Environment on the mental health of older adults. Through field investigation in the sample villages, 515 valid questionnaires were obtained. The results from the Binary Logistic Regression Model show that good marital status, physical health, education level, well-constructed roads, and safe neighborhoods had significant positive effects on the mental health of rural older adults. Rural older adults who prefer to walk, cycle, and use public transport have better mental health, and accessibility to the periodic market, health station, bus station, village committee, supermarket, and the main road is positively correlated with the mental health of rural older adults, while the distance from home to the town center and coach terminal has a significant negative impact on the mental health of rural older adults. The research results provide a theoretical reference for further construction of rural aging environments.

Indexed as

Built EnvironmentMental HealthLogistic ModelsSurveys and Questionnairesbinary logistic modelbuiltmental healtholder adultsrural areas

Identifiers

PMID37427261
PMCPMC10328118

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.