Taking ACTion for anxiety and depression: A pilot study of a brief virtual Acceptance and commitment therapy group in primary care
Bibliographic record
Abstract
Brief Acceptance and Commitment Therapy (ACT) interventions are well-suited to facilitate timely access to effective treatment for patients experiencing common mental health concerns. This study piloted a 4-session virtual ACT group for primary care patients experiencing symptoms of depression and anxiety (n = 21). Changes in symptoms of depression and anxiety, psychological flexibility, and quality of life (QoL) were examined at post-treatment and at 1- and 3-month follow-up. At a group level, there were significant reductions in symptoms of depression for all time points and symptoms of anxiety were significantly reduced at 1- and 3-month follow-up. Measures of psychological flexibility and QoL were significantly improved at 1- and 3-month follow-up. Reliable change index scores revealed high proportions of individual improvement for depression and anxiety symptoms (56–83% across time points). Proportions of reliable change ranged from 25 to 50% for measures of psychological flexibility and QoL at 1- and 3-month follow-up. Overall, these results are comparable with previously reported rates of improvement for empirically supported treatments and provide preliminary evidence for the effectiveness of this brief ACT intervention delivered in a community setting.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".