Evidence map›Paper›PMID 33082738›Full record

ArticleEuropean journal of ageing2021

Understanding unequal ageing: towards a synthesis of intersectionality and life course analyses.

Daniel Holman, Alan Walker

Abstract read
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In one paragraph

Article in European journal of ageing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

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

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  10. Structural determinants of Medicaid enrollment in late life.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026
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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.

Daniel HolmanDepartment of Sociological Studies, The University of Sheffield, Elmfield, Northumberland Road, Sheffield, S10 2TU UK.ORCID 0000-0003-4134-6238
Alan WalkerDepartment of Sociological Studies, The University of Sheffield, Elmfield, Northumberland Road, Sheffield, S10 2TU UK.ORCID 0000-0002-3169-2738

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intersectionality has received an increasing amount of attention in health inequalities research in recent years. It suggests that treating social characteristics separately-mainly age, gender, ethnicity, and socio-economic position-does not match the reality that people simultaneously embody multiple characteristics and are therefore potentially subject to multiple forms of discrimination. Yet the intersectionality literature has paid very little attention to the nature of ageing or the life course, and gerontology has rarely incorporated insights from intersectionality. In this paper, we aim to illustrate how intersectionality might be synthesised with a life course perspective to deliver novel insights into unequal ageing, especially with respect to health. First we provide an overview of how intersectionality can be used in research on inequality, focusing on intersectional subgroups, discrimination, categorisation, and individual heterogeneity. We cover two key approaches-the use of interaction terms in conventional models and multilevel models which are particularly focussed on granular subgroup differences. In advancing a conceptual dialogue with the life course perspective, we discuss the concepts of roles, life stages, transitions, age/cohort, cumulative disadvantage/advantage, and trajectories. We conclude that the synergies between intersectionality and the life course hold exciting opportunities to bring new insights to unequal ageing and its attendant health inequalities.

Indexed as

Cumulative disadvantageHealth inequalitiesIntersectionalityLife courseUnequal ageing

Identifiers

PMID33082738
PMCPMC7561228

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.