Variability Exists Across Outcomes Measured and Reported in Studies Assessing Interventions for Generalized Anxiety Disorder During the Perinatal Period: A Scoping Review
Bibliographic record
Abstract
Objective: This review aimed to identify if heterogeneity exists across outcomes measured and reported in treatment studies targeting perinatal individuals with Generalized Anxiety Disorder (GAD). Design: Scoping review. Setting: Existing literature that evaluates the effectiveness of interventions for GAD during the perinatal period (i.e., pregnant and postpartum). Methods: Studies were eligible if: 1) they were English from the years 2011 to 2021; 2) participants were in the perinatal period with a GAD diagnosis; and 3) the aim of the intervention was to treat GAD during the perinatal period. Three bibliographic databases were searched. Two reviewers independently screened studies for eligibility. Study characteristics and data (e.g., outcomes, OMIs used) were extracted. Main outcome measures: Outcome measures are not required for this type of study design. Results: Of the 4424 records identified, 4 studies were included. The outcomes from the four studies were mapped to one of five core areas in Dodd et al.’s (2018) research outcome framework. A total of 10 distinct outcomes were captured across the 4 studies. Anxiety symptoms, the most common outcome, employed three different OMIs. Notably, the majority of outcomes fell within the physiological/clinical core area, indicating a dearth of patient-centered outcomes in the literature. Conclusions: This review highlights outcome and OMI variations in perinatal GAD studies. To improve synthesis, reproducibility, and comparability among treatment studies, future research in perinatal GAD treatment should adopt standardized outcomes and OMIs. This standardization is essential for informing clinical practice guidelines and policies.
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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 | 0.074 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".