Third‐wave strategies for emotion regulation in early psychosis: a pilot study
Bibliographic record
Abstract
AIM: Emerging evidence supports the priority of integrating emotion regulation strategies in cognitive behaviour therapy for early psychosis, which is a period of intense distress. Therefore, we developed a new treatment for emotional regulation combining third-wave strategies, namely compassion, acceptance, and mindfulness (CAM) for individuals with early psychosis. The purpose of this study was to examine the acceptability, feasibility and potential clinical utility of CAM. METHOD: A non-randomized, non-controlled prospective follow-up study was conducted. Outpatients from the First Psychotic Episode Clinic in Montreal were offered CAM, which consisted of 8-week 60-75 min weekly group sessions. Measures of adherence to medication, symptoms, emotional regulation, distress, insight, social functioning and mindfulness were administered at baseline, post-treatment and at 3-month follow up. A short feedback interview was also conducted after the treatment. RESULTS: Of the 17 individuals who started CAM, 12 (70.6%) completed the therapy. Average class attendance was 77%. Post-treatment feedback indicated that participants found the intervention acceptable and helpful. Quantitative results suggest the intervention was feasible and associated with a large increase in emotional self-regulation, a decrease in psychological symptoms, especially anxiety, depression, and somatic concerns, and improvements in self-care. CONCLUSION: Overall results support the acceptability, feasibility and potential clinical utility of the new developed treatment. A significant increase in emotional self-regulation and a decrease in affective symptoms were found. No significant changes were observed on measures of mindfulness, insight, distress and social functioning. Controlled research is warranted to validate the effectiveness of the new treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".