How Do Psychoanalytic Mental Health Clinicans’ Reactions, Understandings and Formulations Shape Their Work with Gender-Creative LGBTQ+ Clients?
Bibliographic record
Abstract
This study investigates the ways in which psychoanalytic mental health clinicians in North America (United States and Canada) face and manage prejudices targeting gender diversity with LGBTQ+ clients, and what they do or do not do to prevent and repair ruptures in the therapeutic alliance through latrogenic gender enactments. The predominantly white sample was comprised of psychoanalytic clinicians: members of the International Psychoanalytical Association that trained and practices in the United States and Canada, and the years of experience oscillated between 3 and 48 years. There were 20 eligible participants for a 60- to 90-minute semi-structured interview; 14 males and 6 females. Ten participants out of the 20 identified as queer, gay, bisexual and/or trans. The results support current trends in prejudice studies that recommend symbolic relationships with members of the prejudiced-against community to achieve the dismantling of internalized prejudices. It provides evidence of the importance of accessibility at all levels of institutional psychoanalysis to address systemic prejudices that impact the training of beginning clinicians and deter LGBTQ+ people from accessing psychoanalytic treatments that can be beneficial. Lastly, participants strongly advocated for a revision of pedagogic curricula that includes a less harmful conceptualization of gender and sexual diversity.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".