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Record W4404638943 · doi:10.1080/19419899.2024.2433127

Narrative processing of anti-LGBTQ+ victimisation: exploring themes of redemption and meaning

2024· article· en· W4404638943 on OpenAlexaffabout
Alvi M. Dandal, Nic M. Weststrate, J. Roy Gillis

Bibliographic record

VenuePsychology and Sexuality · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVictimisationNarrativePsychologyMeaning (existential)Narrative inquiryPsychoanalysisQueerSocial psychologyStorytellingPsychotherapistPoison controlLiteratureSuicide preventionArt

Abstract

fetched live from OpenAlex

Despite increasing social acceptance, anti-LGBTQ+ victimisation remains a major risk to the well-being of LGBTQ+ people in Canada. Research has robustly shown that anti-LGBTQ+ victimisation is associated with indicators of negative mental health. Research is needed to better understand the ways that LGBTQ+ people adaptively cope with such victimisation experiences. Addressing this need, in the current study, 30 LGBTQ+ adults (19 to 66 years of age; M = 36.87; SD = 11.42) from a large Canadian city provided narrative reconstructions of victimisation experiences. These narratives were content analysed for themes of redemption and meaning making. Somewhat surprisingly, less than half of the sample narrated their experiences with themes of redemption and meaning. When it was present, redemption and meaning manifested in diverse ways across participants. Examples of redemptive themes included achieving a sense of justice, expressing generative concern for the welfare of other people, and finding a deeper level of self-acceptance. Examples of meaning making included lessons about personal safety, new positive and negative self-understandings, and wisdom into how to deal with life. The significance of these results is discussed within the context of psychotherapy with anti-LGBTQ+ victims.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.165
GPT teacher head0.455
Teacher spread0.290 · 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 designObservational
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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