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Record W4391377740 · doi:10.21926/obm.geriatr.2401267

The Indigo Project: Participatory Action Research with Gender and Sexual Minority Survivors of Elder Abuse

2024· article· en· W4391377740 on OpenAlexafffund
Claire Robson, Jen Marchbank, Gloria Gutman

Bibliographic record

VenueOBM Geriatrics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsSimon Fraser University
FundersVancouver Foundation
KeywordsParticipatory action researchAction (physics)Citizen journalismElder abuseSexual abusePsychologyClinical psychologyMedicineSociologyPolitical sciencePoison controlSuicide preventionEnvironmental health

Abstract

fetched live from OpenAlex

Though research suggests that older adults belonging to gender and sexual minorities (GSM) are at greater risk of abuse and neglect, more needs to be done to investigate this situation, provide solid data, offer support to survivors and better inform those providing services. This article reports on a participatory action research project in which nine older adults with lived experience of abuse were interviewed, as were the seniors’ programmer from our community partner organization and a trauma counsellor who supported our participants throughout the project. Participants were interviewed at least twice, often more, and the resulting interview transcripts were edited with the help and consent of the participant concerned, to form narratives which were content-analyzed.<em> </em>The goals of the project were to raise awareness of the underreported issue of abuse of elder GSM individuals, to consider how elder abuse might both differ and look the same as it does in the mainstream population, and to offer mental health supports and safe spaces for healing for our participants. This deep dive into lived experience illuminates how homophobia and transphobia (both historic and contemporary) play out in subtle and complex ways. We conclude with recommendations for researchers and care/service providers.

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)0.002
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 score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.229
GPT teacher head0.433
Teacher spread0.203 · 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
Published2024
Admission routes2
Has abstractyes

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