An Online Educational Program for Individuals With Dissociative Disorders and Their Clinicians: 1‐Year and 2‐Year Follow‐Up
Bibliographic record
Abstract
Individuals with dissociative disorders (DDs) are underrecognized, underserved, and often severely psychiatrically ill, characterized by marked dissociative and posttraumatic stress disorder (PTSD) symptoms with significant disability. Patients with DD have high rates of nonsuicidal self-injury (NSSI) and suicide attempts. Despite this, there is a dearth of training about DDs. We report the outcome of a web-based psychoeducational intervention for an international sample of 111 patients diagnosed with dissociative identity disorder (DID) or other complex DDs. The Treatment of Patients with Dissociative Disorders Network (TOP DD Network) program was designed to investigate whether, over the course of a web-based psychoeducational program, DD patients would exhibit improved functioning and decreased symptoms, including among patients typically excluded from treatment studies for safety reasons. Using video, written, and behavioral practice exercises, the TOP DD Network program provided therapists and patients with education about DDs as well as skills for improving emotion regulation, managing safety issues, and decreasing symptoms. Participation was associated with reductions in dissociation and PTSD symptoms, improved emotion regulation, and higher adaptive capacities, with overall sample |d|s = 0.44-0.90, as well as reduced NSSI. The improvements in NSSI among the most self-injurious patients were particularly striking. Although all patient groups showed significant improvements, individuals with higher levels of dissociation demonstrated greater and faster improvement compared to those lower in dissociation |d|s = 0.54-1.04 vs. |d|s = 0.24-0.75, respectively. These findings support dissemination of DD treatment training and initiation of treatment studies with randomized controlled designs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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 | 0.001 |
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".