Helpful and meaningful aspects of a psychoeducational programme to treat complex dissociative disorders: a qualitative approach
Bibliographic record
Abstract
Purpose: Complex dissociative disorders (CDDs) are prevalent among psychotherapy clients, and research suggests carefully paced treatment for CDDs is helpful. The purpose of the present study is to qualitatively explore helpful and meaningful aspects of the TOP DD Network programme, a web-based adjunctive psychoeducational programme for the psychotherapeutic treatment of clients with CDDs.Methods: TOP DD Network programme participants (88 clients and 113 therapists) identified helpful and meaningful aspects of their participation in response to two open textbox questions. Framework analysis was used to qualitatively analyze client and therapist responses.Findings: Participants found the TOP DD Network programme helpful and meaningful in nuanced ways. Three themes were created: (1) Components of the Programme (subthemes: content, structure), (2) Change-Facilitating Processes (subthemes: heightened human connection, receiving external empathy and compassion, contributing to something bigger, improved therapeutic work and relationship), and (3) Outcomes (subthemes: insight, increased hope, self-compassion, increased safety and functioning). The most emphasized theme was components of the programme, which captured its content and structure.Conclusion: Clients and therapists in the TOP DD Network programme described the programme’s components and processes as helpfully facilitating positive outcomes in the treatment of CDDs. Therapists may consider integrating the components and processes in the programme into their practice with clients with CDDs.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".