Sex and gender in treatment response to dialectical behaviour therapy: current knowledge, gaps, and future directions
Bibliographic record
Abstract
Abstract Borderline personality disorder (BPD) is a mental health condition characterized by emotion dysregulation, interpersonal impairment, and high suicidality. Dialectical behaviour therapy (DBT) is the most widely studied psychotherapeutic treatment for BPD. To date, the vast majority of DBT research has focused on cisgender women, with a notable lack of systematic investigation of sex and/or gender differences in treatment response. In order to encourage effective, equitable treatment of BPD, further investigation into treatment targets in this population is critical. Here, we employed a systematic strategy to delineate gaps in the DBT literature pertaining to sex and gender differences and propose directions for future research. Findings demonstrate a significant discrepancy in measurement of sex and gender, particularly among gender-diverse individuals. Exploring DBT treatment response across the full spectrum of genders will facilitate the provision of more tailored, impactful care to all individuals who suffer from BPD. Key learning aims (1) To date, DBT treatment literature has focused almost exclusively on cisgender women, with only two of 253 DBT studies in current literature accounting for transgender and gender diverse (TGD) individuals. (2) Recognize how gender minority stress may impact the prevalence of BPD among TGD individuals. (3) Learn how future research initiatives can be employed to rectify this gap in the DBT literature.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".