Evidence map›Paper›PMID 31562589›Full record

ArticleCommunity mental health journal2020

Exploring Community Integration Among Formerly Homeless Veterans in Project-Based Versus Tenant-Based Supportive Housing.

Melissa Chinchilla, Sonya Gabrielian, Amy Glasmeier, Michael F Green

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Article in Community mental health journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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.

Melissa ChinchillaAltaMed Institute for Health Equity, 2040 Camfield Ave, Los Angeles, CA, 90040, USA. mechinchilla@altamed.org.ORCID 0000-0001-7546-0827
Sonya GabrielianDepartment of Veteran Affairs Greater Los Angeles Center for the Study of Healthcare Innovation, Implementation, & Policy (CSHIIP), 11301 Wilshire Blvd, Los Angeles, CA, 90073, USA.
Amy GlasmeierDepartment of Urban Studies and Planning at Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Michael F GreenDepartment of Psychiatry and Biobehavioral Sciences at University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA, 90095, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Community integration-an individual's embeddedness in his/her community-impacts mental and physical health. This study aimed to understand factors affecting community integration among Veterans in the Department of Housing and Urban Development-VA Supportive Housing (HUD-VASH) program. Semi-structured interviews and focus groups were conducted with HUD-VASH staff (n = 14) and persons residing in project-based (n = 9) and tenant-based (n = 9) housing at VA Greater Los Angeles. Participants identified neighborhood safety concerns as a limitation to community integration. Participants were reluctant to connect with HUD-VASH peers living nearby because they wanted to focus on their own recovery (e.g., from substance use); and many were dissatisfied with the location of their apartments. Staff valued community integration but saw it as secondary to housing retention. Increased access to safe neighborhoods (e.g., through relationship building with landlords) and the addition of staff dedicated to improving community integration (e.g., peer-support specialists) would enhance community integration in the HUD-VASH program.

Indexed as

Ill-Housed PersonsVeteransCommunity IntegrationFemaleHousingHumansLos AngelesMalePublic HousingUnited StatesUnited States Department of Veterans AffairsCommunity integrationHomelessnessHUD-VASHPermanent supportive housingVeterans

Identifiers

What OpenQuestion holds

Textmetadata
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.