Evidence map›Paper›PMID 36424283›Full record

ArticleSocial science & medicine (1982)2023

Ageism and late-life mortality: How community matters.

Deborah Carr

Open access · greenAbstract read
In one paragraph

Article in Social science & medicine (1982), 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
2.8field-weighted citation impact, top 10% 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, 8 citations in OpenAlex.

  1. Article
  2. 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

1 author at 1 institution in 1 country.

Deborah CarrDepartment of Sociology and Center for Innovation in Social Science, Boston University, 704 Commonwealth Ave, Boston, MA, 02215, USA. Electronic address: Carrds@bu.edu.
Center for Innovation · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ageismthe deeply entrenched biases that people hold about old age-is a persistent social problem that intensified during the COVID-19 pandemic. The harmful physical, emotional, and cognitive health consequences of individual-level age bias are well-documented, with most studies operationalizing ageism as an older adult's personal encounters with age discrimination, self-perceptions of their own aging, and internalized negative beliefs about old age. However, the impacts of community-level age bias on older adults' well-being have received less attention. This commentary reviews recent evidence (Kellogg et al.,) showing that county-level explicit age bias is associated with lower mortality rates among older adults, with effects limited to older adults residing in counties with relatively younger populations. Effects were not detected in counties with relatively older populations, or for implicit age bias. These counterintuitive findings require further exploration, including the use of more fine-grained measures of community-level ageism, attention to the role of gentrification in communities, and the development of new measures of structural ageism, drawing on approaches used to study the impacts of structural racism. Data science approaches, including the use of social media data in tandem with mortality data, may reveal how age bias affects older adults. Communities are especially important to older adults, who spend much of their time in areas immediately proximate to their homes. As more individuals age in place, and as federal funding for home-based and community services (HCBS) increases, researchers should identify which community-level characteristics, including age bias, undermine or enhance late-life well-being.

Indexed as

AgeismCOVID-19AgedAgingHumansPandemicsSelf Concept

Identifiers

PMID36424283
PMCPMC9678335
OpenAlexW4309535311

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.