Antenatal group therapy improves worry and depression symptoms.
Bibliographic record
Abstract
INTRODUCTION: Antenatal anxiety and depression occur in approximately 20% of pregnant women with potentially deleterious effects to mother and child. While effective in reducing symptoms, some pregnant women are often reluctant to take psychotropic medications. We tested the effectiveness of group therapy to provide worry and depression symptom relief. METHODS: Women (N=38) in 15-28 weeks of gestation were recruited to antenatal Psychotherapy Groups using either interpersonal or mindfulness based therapy. We collected data at three times, upon intake to and at completion of the group and at four weeks postpartum. Descriptive, Chi-square, and GEE analyses were used to compare depression and worry symptoms with a matched control group of pregnant women (N=68). LIMITATIONS: Small sample size in both groups required a matched control group with no randomization. RESULTS: Attending group therapy significantly reduced worry and depression symptoms over pregnancy into the postpartum compared to women receiving no therapy. There was no difference in symptom reduction between different types of groups attended. DISCUSSION: Engaging pregnant women in group therapy can significantly improve worry and depression symptoms, with lasting effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".