Evaluation of an Augmented Cognitive Behavioural Group Therapy for Perinatal Generalized Anxiety Disorder (GAD) during the COVID-19 Pandemic
Bibliographic record
Abstract
The perinatal period is considered a window of vulnerability given the increased risk of psychiatric difficulties during this time, such as mood and anxiety disorders (ADs). Pre-pandemic rates of ADs in perinatal women were one in five but have since increased with the onset of the COVID-19 pandemic (COVID). In addition, recent research suggests that the focus of worry has shifted during the pandemic, with perinatal women reporting significantly more COVID-specific worries. The objective of this study was to augment our current evidence-based Cognitive Behavioural Group Therapy (CBGT) for perinatal anxiety protocol by targeting intolerance of uncertainty and tailoring existing strategies to address COVID-related worry and impact. Pregnant (n = 19) and postpartum (n = 49) women were recruited from regular clinic patient flow from a university-affiliated teaching hospital between September 2020 and March 2021. Improvements in generalized anxiety symptoms, worry, intolerance of uncertainty, and mood were observed at post-treatment, maintained at 3-months, and the intervention received high ratings of treatment satisfaction. This is the first study to examine an augmented CBGT for perinatal women with GAD during the pandemic and supports the inclusion of strategies that target intolerance of uncertainty as well as specific pandemic and perinatal worry content for effective outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".