{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:07:09Z","timestamp":1793156829194,"version":"4.5.0"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Despite extensive literature that examines the relationship between discrimination and health, less is known about specific discrimination attributions and how they may differentially associate with health. To address this gap, the current study investigated the latent typology of discrimination attributions and the intersectional attributions\u2019 relationships with mental health in later life.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data came from 6,282 respondents in the 2016 Psychosocial Leave-Behind Questionnaire of the Health and Retirement Study. Participants ascribed their everyday discrimination experiences to a list of potential reasons (e.g., ethnicity, ancestry, gender, race, age, religion, and financial). Latent class analysis was performed to identify discrimination attribution typologies. Regression models with marginal effects were conducted to explore differential health associations of different attribution typologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five distinct typologies were identified: few discrimination experiences (33%), discrimination with no specified attributes (5%), discrimination due to age (48%), discrimination due to age, race, and ethnicity (8%), and discrimination due to age, explicit physical characteristics, and socioeconomic disadvantages (5%). Regression analysis revealed significant associations between discrimination and mental health indicators such as depressive symptoms and loneliness. Discrimination involving more than just age, especially physical and socioeconomic disadvantages, had strong negative associations with health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Ageism emerged as a core dimension and prevalent theme and often co-occurs with other characteristics, highlighting the intersectionality of perceived discrimination. The negative health associations were most pronounced for those who experienced discrimination related to intersectional attributions. Implications for social policies, practice, and research were discussed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/geronb\/gbaf184","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:46:55Z","timestamp":1758628015000},"source":"Crossref","is-referenced-by-count":2,"title":["Intersectional discrimination and mental health in later life: ageism as a core dimension"],"prefix":"10.1093","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1359-5712","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Social Work, University of Iowa , Iowa City, Iowa,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-324X","authenticated-orcid":false,"given":"Yifan","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Social Work, Virginia Commonwealth University , Richmond, 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