Prevalence of Dissociative Disorders in People Suffering from Gender Dysphoria. A Review of Literature
Bibliographic record
Abstract
Literature showed an increased prevalence of mental disorders in people suffering from gender dysphoria (GD). Moreover, current guidelines stressing the importance of mental health assessment in GD treatment, don't mention the assessment of dissociative disorders (DD). Due to the paucity of information concerning DD in people suffering from GD, we decided to provide a review of the literature regarding the prevalence of DD in GD. A systematic review of the literature was performed in Pubmed, PsychInfo, and Embase databases. During the literature review, 10 articles were included. 4 of them were “case-control” studies, with important cohorts, all reporting a higher prevalence of DD in GD compared to controls, respectively from 1,3% to 16,3%. 4 of them were “case” studies that reported respectively 1,2%; 1,5%; 11%; 29,6% of DD in GD. 2 other studies didn't directly respond to the prevalence of DD. This article provides the first systematic review on GD and DD and shows a higher prevalence of DD in people suffering from GD, than in the general population. The results of this review should emphasize the fact that mental health professionals who work with people suffering from GD should consider DD as a possible comorbidity, in order to improve the treatment given to these patients. Moreover, some authors mention the difficulty to establish DD in the context of GD, because of the similarities between the two conditions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".