LGBTQ+ Patients’ Experiences of Harm in Psychotherapy: A Scoping Review & Reflexive Thematic Analysis of the Peer-Reviewed Literature
Bibliographic record
Abstract
Harmful experiences in therapy, such as microaggressions and sexual boundary violations, occur more frequently than is commonly thought. Existing research shows that while LGBTQ+ people seek out psychotherapy in greater proportion than their heterosexual and cisgender counterparts, they also tend to report more harmful experiences in therapy. Harmful experiences in therapy negatively affect therapy outcomes and may worsen patients’ psychological well-being. Therapists also have a long history of harm against sexual and gender minority patients. In order to improve the safety and efficacy of psychotherapy for LGBTQ+ patients, a better understanding of their harmful experiences in therapy is needed. In this thesis, I first conducted a scoping review to identify the relevant peer-reviewed literature on LGBTQ+ patients’ experiences of harm in therapy. Our review identified 61 articles based on the inclusion criteria. Articles’ participant demographic data, focus population, and publishing information was charted. To identify forms of harm, I further conducted a Reflexive Thematic Analysis on the articles extracted during the scoping review. Through this process, I generated three superordinate themes: (1) conversion therapy-related harms; (2) discrimination and prejudice in psychotherapy; and (3) heterosexist and cissexist enactments and misattunements. These results are discussed in light of current events and the related peer-reviewed literature, noting increases in conversion therapy targeting trans and nonbinary people and anti-trans legislation. In addition, I also highlighted several gaps in the extracted literature, including insufficient attention to major ethical breaches, such as violations of confidentiality and sexual boundary violations. Finally, I note implications of these results for clinical practice and training.
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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 | 0.069 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".