Cisheteronormativity, Conversion Therapy, and Identity Among Sexual and Gender Minority People: A Narrative Inquiry and Creative Non-fiction
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".