A pragmatic randomized controlled trial of a group self-management support program versus treatment-as-usual for anxiety disorders: study protocol
Bibliographic record
Abstract
BACKGROUND: The integration of a personal recovery-oriented practice in mental health services is an emerging principle in policy planning. Self-management support (SMS) is an intervention promoting recovery that aims at educating patients on the nature of their mental disorder, improving their strategies to manage their day-to-day symptoms, fostering self-efficacy and empowerment, preventing relapse, and promoting well-being. While SMS is well established for chronic physical conditions, there is a lack of evidence to support the implementation of structured SMS programs for common mental disorders, and particularly for anxiety disorders. This study aims to examine the effectiveness of a group-based self-management support program for anxiety disorders as an add-on to treatment-as-usual in community-based care settings. METHODS/DESIGN: We will conduct a multicentre pragmatic randomized controlled trial with a pre-treatment, post-treatment (4-month post-randomization), and follow-ups at 8, 12 and 24-months. TREATMENT AND CONTROL GROUPS: a) group self-management support (10 weekly 2.5-h group web-based sessions with 10-15 patients with two trained facilitators); b) treatment-as-usual. Participants will include adults meeting DSM-5 criteria for Panic Disorder, Agoraphobia, Social Anxiety Disorder, and/or Generalized Anxiety Disorder. The primary outcome measure will be the Beck Anxiety Inventory; secondary outcome measures will comprise self-reported instruments for anxiety and depressive symptoms, recovery, self-management, quality of life, and service utilisation. STATISTICAL ANALYSIS: Data will be analysed based on intention-to-treat with a mixed effects regression model accounting for between and within-subject variations in the effects of the intervention. DISCUSSION: This study will contribute to the limited knowledge base regarding the effectiveness of structured group self-management support for anxiety disorders. It is expected that changes in patients' self-management behaviour will lead to better anxiety management and, consequently, to improved patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05124639 . Prospectively registered 18 November 2021.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".