Targeting Intolerance of Uncertainty During Pregnancy
Bibliographic record
Abstract
Postpartum anxiety is common, often underrecognized, and associated with numerous negative outcomes for both the perinatal individual and their infant. Despite its high prevalence and burden, research focused on preventative strategies for postpartum anxiety remains very limited. This study investigated whether a 6-week cognitive behavioral therapy protocol targeting intolerance of uncertainty (CBT-IU) during pregnancy could reduce risk for postpartum anxiety disorders among individuals with heightened intolerance of uncertainty (IU). In this investigator-initiated, single-site, proof-of-concept, randomized controlled trial (RCT), eligible participants (n=37), between 14 and 32 weeks' gestation, with heightened IU (baseline score of ≥64 on the 27-item Intolerance of Uncertainty Scale) were randomized to a 6-session individual CBT-IU or care as usual (CAU), of whom 35 completed measures and were included in analyses. The primary objective of this study was to evaluate whether CBT-IU for pregnant individuals with elevated IU could reduce the risk of postpartum anxiety disorder compared to CAU. Secondary outcomes included changes in worry, depression, and emotion regulation. <.001) compared to CAU. Treatment satisfaction among CBT-IU participants was high. These findings suggest that targeting IU during pregnancy may be an effective preventive strategy for reducing the risk of postpartum anxiety disorder onset. This proof-of-concept RCT supports a large-scale RCT to ultimately test CBT-IU as an effective intervention for prevention of postpartum anxiety disorders. ClinicalTrials.gov identifier: NCT05691140.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".