31.2 BE OUTSPOKEN AND OVERCOME STIGMATIZING THOUGHTS (BOOST): A GROUP TREATMENT FOR INTERNALIZED STIGMA IN FIRST-EPISODE PSYCHOSIS
Bibliographic record
Abstract
Nearly half of individuals experiencing psychotic disorders report moderate to high levels of internalized stigma, which can significantly interfere with the recovery goals of patients. Despite a growing awareness of the negative clinical outcomes of internalized stigma, few interventions have been designed to specifically address this issue in first episode psychosis. Therefore, the goal of the present study was to examine the efficacy of a novel treatment for this specialized population: BOOST (Be Outspoken and Overcome Stigmatizing Thoughts). BOOST is an eight-session group intervention that combines cognitive restructuring, assertive communication skills, and peer support. The group is co-facilitated by a peer support worker and the development of the intervention integrated service users. Participants (N = 15) recruited from an early psychosis intervention clinic received BOOST in a pilot open-label study. Pre- and post-treatment measures included the Internalized Stigma of Mental Illness scale, the Rosenberg Self-Esteem Scale, and the Satisfaction with Life Scale. BOOST significantly improved internalized stigma, p = .04, Cohen’s d = .76; self-esteem, p = .02, Cohen’s d = 1.2; and satisfaction with life, p = .03; Cohen’s d = 1.2. Results from this pilot study suggest that in addition to reducing internalized stigma, BOOST effects might transfer to other proximal and distal outcome measures. We will also present the results of a dissemination project within a large-scale psychosis network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".