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Record W4390081195 · doi:10.1093/geroni/igad104.2130

THROUGH THE LOOKING-GLASS: THE FAMILY SHAME OF ELDER ABUSE

2023· article· en· W4390081195 on OpenAlexaff
Jessica Hsieh, Raza Mirza

Bibliographic record

VenueInnovation in Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsShameFeelingBlameThematic analysisPsychologyQualitative researchClinical psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Elder abuse (EA) has been recognized as a serious public health concern. Recent studies have found that approximately 10% of community-dwelling, cognitively intact older adults experience some form of EA each year. Although EA research has made substantial progress, EA is often under-reported, with only an estimated 15% of cases being reported to formal support services. One of the main reasons for the under-reporting of EA is the victims’ feelings of shame, which have been shown to be exacerbated when the perpetrator is a family member and/or an individual with whom the older adults have close trusting relationships. Qualitative interviews conducted with 12 older adult (parent) and adult child caregiver dyads (n=24) revealed that older adults who experience EA by their adult children experience intense shame. Thematic analysis focused on what led to these feelings of shame, and this resulted in four main themes: (1) Failure in their role as a parent; (2) Adult children viewing them as powerless and unworthy; (3) Experiencing negative psychological effects; and (4) Self-blame. With a sense of responsibility to protect their family, older adults tend to keep ‘family shame’ to themselves, leading to a reluctance to disclose EA. As spouses and adult children, the two most common caregiving types, have also been reported to be the two most common groups of EA perpetrators, gaining a better understanding of the root causes of shame can help to support older adults in living safely in their homes and communities for as long as possible.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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