The impact of peer-delivered cognitive behavioral therapy for postpartum depression on infant emotion regulation
Bibliographic record
Abstract
BACKGROUND: Postpartum depression (PPD) affects up to one in five and negatively affects mothers, birthing parents, and their infants. The impact of PPD exposure on infant emotion regulation (ER) may be particularly harmful given its associations with later psychiatric problems. It remains unclear if treating maternal PPD can improve infant ER. OBJECTIVE: To examine the impact of a nine-week peer-delivered group cognitive behavioral therapy (CBT) intervention on infant ER assessed across physiological and behavioral levels. METHODS: Seventy-three mother-infant dyads were enrolled in a randomized controlled trial from 2018 to 2020. Mothers/birthing parents were randomized to the experimental group or waitlist control group. Measures of infant ER were collected at baseline (T1) and nine weeks later (T2). Infant ER was assessed using two physiological measures (frontal alpha asymmetry (FAA) and High Frequency-Heart Rate Variability (HF-HRV)), and parental-report of infant temperament. RESULTS: Experimental group infants displayed more adaptive changes in both physiological markers of infant ER from T1 to T2 (FAA (F(1,56) = 4.16, p = .046) and HF-HRV (F(1,28.1) = 5.57, p = .03)) than those in the waitlist control group. Despite improvements in maternal PPD, no differences were noted in infant temperament from T1 to T2. LIMITATIONS: A limited sample size, potential lack of generalizability of our results to other populations, and an absence of long-term data collection. CONCLUSIONS: A scalable intervention designed for those with PPD may be capable of adaptively improving infant ER. Replication in larger samples is needed to determine if maternal treatment can help disrupt the transmission of psychiatric risk from mothers/birthing parents to their infants.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".