Article in Stigma and health, 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
6.3field-weighted citation impact, top 3% 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, 12 citations in OpenAlex.
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
5 authors at 3 institutions in 1 country.
E-Shien ChangDivision of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, New York, United States of America.
Joan K MoninSocial and Behavioral Sciences Department, Yale School of Public Health, New Haven, Connecticut, United States of America.
Naomi IsenbergDepartment of Psychology, University of Wisconsin- Madison, Wisconsin, United States of America.
Daniel ZeltermanDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
Becca R LevySocial and Behavioral Sciences Department, Yale School of Public Health, New Haven, Connecticut, United States of America.
Yale University · USCornell University · USUniversity of Wisconsin–Madison · US
Funding
Yale Pathology Tissue Services Shared ResourceP30CA016359 · NCI · YALE UNIVERSITY · PI DAVID L RIMM · 1985 to 2026
$85.0M
Yale SPORE in Lung Cancer (YSILC): The Biology and Personalized Treatment of Lung CancerP50CA196530 · NCI · YALE UNIVERSITY · PI Shuangge Ma · 2015 to 2026
$31.1M
Pilot CoreP30AG022845 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Manney Carrington Reid · 2003 to 2026
$15.2M
Oncometabolites, DNA Repair, and CancerR01ES005775 · NIEHS · YALE UNIVERSITY · PI PETER M GLAZER · 1998 to 2026
$8.0M
Cognitive Resilience among Older SamoansR01AG067533 · NIA · YALE UNIVERSITY · PI LEVY, BECCA R · 2021 to 2025
$4.8M
Attachment Behaviors in Parent Child Dyads Coping with Early Stage Alzheimer's Disease and Related DementiasR01AG058565 · NIA · YALE UNIVERSITY · PI MONIN, JOAN E. · 2019 to 2023
$3.9M
The Weill Cornell Medicine Research Training Program in Behavioral GeriatricsT32AG049666 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI SARA J CZAJA, Manney Carrington Reid · 2016 to 2026
$3.2M
Reid Aging and Pain Research Training ProgramK24AG053462 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI REID, MANNEY CARRINGTON · 2016 to 2025
$1.8M
Cell-Penetrating Anti-DNA Antibody for Radiosensitization and Cancer TherapyR01CA168733 · NCI · YALE UNIVERSITY · PI GLAZER, PETER M · 2013 to 2017
$1.7M
Culture-Gene Relationship: A Novel Model of Aging Cognitive HealthU01AG032284 · NIA · YALE UNIVERSITY · PI LEVY, BECCA R · 2017 to 2020
$1.7M
Bifunctional Antibodies for Melanoma TherapyR01CA177719 · NCI · YALE UNIVERSITY · PI GLAZER, PETER M · 2014 to 2016
$1.3M
A Daily Self-Regulation Intervention for Persons with Early Stage Alzheimer's Disease and Related Dementias and their SpousesR21AG055861 · NIA · YALE UNIVERSITY · PI MONIN, JOAN E. · 2018 to 2019
Elder abuse affects one in six older persons globally. Three limitations impede progress in prevention: most research is victim- rather than perpetrator-based; the reliance on explicit, self-reported factors; and failure to account for psychological factors, such as dehumanization, that motivate abuse. The current study addressed these gaps by examining whether implicit and explicit dehumanization of t could explain elder abuse proclivity. In a web-based survey of 585 family caregivers of older persons, dehumanization was found to be prevalent with 51% of the caregivers implicitly and 31% explicitly dehumanizing older persons. As predicted, implicit and explicit dehumanization contributed to elder abuse proclivity (OR = 1.23, 95% CI = 1.02-1.50,
Indexed as
ageismelder abusefamily caregivingnegative age stereotypesviolence prevention
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.
Implicit and Explicit Dehumanization of Older Family Members: Novel Determinants of Elder Abuse Proclivity. · full record | OpenQuestion