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Estimated Incidence and Factors Associated With Risk of Elder Mistreatment in New York State

2021· article· en· W3188240383 on OpenAlex
David Burnes, David Hancock, John Eckenrode, Mark S. Lachs, Karl Pillemer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Network Open · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
FundersNational Institute on AgingNational Institutes of Health
KeywordsElder abusePsychosocialPopulationLandlineMedicineGerontologyPsychological abuseContext (archaeology)Physical abuseDemographyPoison controlSuicide preventionDomestic violencePsychiatryMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Elder mistreatment is associated with major health and psychosocial consequences and is recognized by clinicians, policy makers, and researchers as a pervasive problem affecting a rapidly aging global population. Objective: To estimate the incidence of elder mistreatment and identify factors associated with the risk of new cases. Design, Setting, and Participants: This research is a 10-year, longitudinal, population-based, cohort study of the incidence of elder mistreatment in New York State households conducted between 2009 (wave 1) and 2019 (wave 2). At wave 1, random digit-dial (landline and cellular telephones) stratified sampling was done to recruit English-speaking and/or Spanish-speaking, cognitively intact, community-dwelling older adults (aged ≥60 years) across New York State. The current study conducted computer-assisted telephone interviews with older adults who participated in wave 1 and gave permission to be contacted again for wave 2 interviews (response rate, 60.7%). Data analysis was performed from October 2020 to January 2021. Exposures: Physical factors (health status, functional capacity, and age), living arrangement (coresidence), and sociocultural characteristics (sex, race/ethnicity, geocultural context, and household income). Main Outcomes and Measures: Ten-year incidence for overall elder mistreatment and subtypes (financial abuse, emotional or psychological abuse, physical abuse, and neglect) were measured using adapted versions of the Conflict Tactics Scale, the Duke Older Americans Resources and Services scale, and the New York State Elder Mistreatment Prevalence Study financial abuse tool. Results: The analytical sample included 628 older adults (mean [SD] age at wave 1, 69.20 [6.95] years; age at wave 2, 79.40 [6.93] years; 504 non-Hispanic White individuals [80.9%]; 406 women [64.6%]). Ten-year incidence rates were 11.4% (95% CI, 8.8%-14.3%) for overall elder mistreatment, 8.5% (95% CI, 6.3%-10.9%) for financial abuse, 4.1% (95% CI, 2.6%-5.7%) for emotional abuse, 2.3% (95% CI, 1.2%-3.6%) for physical abuse, and 1.0% (95% CI, 0.3%-1.8%) for neglect. Poor self-rated health at wave 1 was associated with increased risk at wave 2 of new overall mistreatment (odds ratio [OR], 2.86; 95% CI, 1.35-5.84), emotional abuse (OR, 3.67; 95% CI, 1.15-11.15), physical abuse (OR, 4.21; 95% CI, 1.14-13.70), and financial abuse (OR, 2.80; 95% CI, 1.16-6.38). Compared with non-Hispanic White participants, Black participants were at heightened risk of overall mistreatment (OR, 2.61; 95% CI, 1.16-5.70) and financial abuse (OR, 2.80; 95% CI, 1.09-6.91). A change from coresidence to living alone was associated with increased risk of financial abuse (OR, 2.74; 95% CI, 1.01-7.21). Conclusions and Relevance: These findings suggest that health care visits may be important opportunities to detect older adults who are at risk of mistreatment. Race is highlighted as an important social determinant for elder mistreatment requiring urgent attention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.315
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it