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Record W4405771384 · doi:10.1080/07317115.2024.2445028

Distressing Aspects of Elder Abuse Victimization: Perspective of Survivors

2024· article· en· W4405771384 on OpenAlexaff
David Burnes, Andie MacNeil, Jessica Hsieh, Isabel Rollandi, Clara Scher, Paula Zanotti, Olivia Fiallo, Clémentine Rotsaert, Jo Anne Sirey, Mark S. Lachs

Bibliographic record

VenueClinical Gerontologist · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
FundersNational Institute of Justice
KeywordsDistressingPerspective (graphical)Elder abusePsychologyPerceptionSuicide preventionClinical psychologyPoison controlMedicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVES: Our understanding of elder abuse (EA) phenomena has largely been shaped from the perspective of researchers and professionals whose conceptualizations often differ from the perceptions of older adults who experience mistreatment. This study sought to understand the most distressing aspects of EA victimization from the perspective of survivors. METHODS: = 32) of EA survivors, recruited from EA support and Adult Protective Services programs in New York City and Los Angeles. Analysis followed a constant comparison process involving two independent coders to understand distressing aspects of EA victimization. RESULTS: The following themes emerged as the most distressing aspects of EA victimization: fear, disbelief, disrespect, concern for perpetrator and other family members, feelings of loss, and incongruity between survivor wishes and systemic responses. Distressing aspects of EA victimization spanned personal, relational, and systemic levels of ecological influence. CONCLUSIONS: Findings from this study advance basic knowledge on EA phenomena and carry direct implications for programs designed to support and meet the needs of survivors. CLINICAL IMPLICATIONS: Findings identify particularly distressing psycho-emotional aspects of EA victimization for clinicians interacting with survivors that can serve as targets of intervention.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.085
GPT teacher head0.443
Teacher spread0.358 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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