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Record W4296328120 · doi:10.1177/10497323221126536

Cisheteronormativity, Conversion Therapy, and Identity Among Sexual and Gender Minority People: A Narrative Inquiry and Creative Non-fiction

2022· article· en· W4296328120 on OpenAlexafffundabout
David J. Kinitz, Travis Salway

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsSimon Fraser UniversityBC Centre for Disease ControlPublic Health OntarioUniversity of Toronto
FundersSimon Fraser University
KeywordsNarrativeFeelingOppressionIdentity (music)Narrative inquiryGender studiesPsychologyMental healthNarrative therapySocial psychologyPsychotherapistSociologyPolitical scienceAestheticsPoliticsArtLiterature

Abstract

fetched live from OpenAlex

Sexual and gender minorities (SGMs) navigate systems of oppression that reify cisgender and heterosexual norms (cisheteronormativity) while developing their identities. 'Conversion therapy' represents a particularly prominent and harmful threat in this landscape. We explore how SGM who experienced conversion therapy develop their identities to understand antecedents to mental health struggles in this population. In-depth interviews were conducted with 22 people in Canada. A 'master narratives' framework combined with Polkinghorne's narrative analysis were used to explore individual-structural relations that affect identity in settings where cisheteronormative master narratives are amplified (i.e., conversion therapy). We present research findings through a creative non-fiction, which includes learning cisheteronormative master narratives; internalizing master narratives; feeling broken and searching for alternatives; and embracing self-love amidst pain. The amplification of master narratives through conversion therapy leads to conflict and delays in adopting a coherent identity. Health professionals should enact institutional practices that affirm SGM and thereby deemphasize cisheteronormativity.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.436
GPT teacher head0.585
Teacher spread0.149 · 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

Citations11
Published2022
Admission routes3
Has abstractyes

Explore more

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