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Record W4390609047 · doi:10.1111/jar.13196

Exploring the complex cognitive, affective and behavioural processes of individuals with intellectual disabilities in financially abusive situations

2024· article· en· W4390609047 on OpenAlexaff
Golnaz Ghaderi, Virginie Cobigo

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual disabilityPsychologyAutonomyCognitionThematic analysisBorderline intellectual functioningDevelopmental psychologyClinical psychologyQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding the cognitive processes of individuals with intellectual disabilities in financially abusive situations is critical to develop effective prevention strategies. AIMS: This study investigated how persons with intellectual disabilities define and analyse financially abusive situations, and how they would feel and act in situations that they consider abusive. MATERIALS AND METHODS: Twelve participants with intellectual disabilities participated in a semi-structured interview where they were asked to reflect on three vignettes illustrating financial abuse. We analysed the interviews using thematic analysis. FINDINGS: The findings revealed that individuals with intellectual disabilities considered the type of relationship between the victims and the perpetrators, the behavioural patterns of the perpetrators, and their own experiences when interpreting the situation. Furthermore, they discussed their emotional and behavioural reactions to the vignettes. CONCLUSION: This study has important implications in supporting the autonomy and decision-making rights of persons with intellectual disabilities regarding their finances and developing effective preventions against financial abuse among this population.

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)0.003
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.308
GPT teacher head0.408
Teacher spread0.100 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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