Public health nurse-delivered cognitive behavioral therapy for postpartum depression: Assessing the effects of maternal treatment on infant emotion regulation
Bibliographic record
Abstract
Abstract The effects of maternal postpartum depression (PPD) on offspring emotion regulation (ER) are particularly deleterious as difficulties with ER predict an increased risk of psychopathology. This study examined the impact of maternal participation in a public health nurse (PHN)-delivered group cognitive behavioral therapy (CBT) intervention on infant ER. Mothers/birthing parents were ≥ 18 years old with an Edinburgh Postnatal Depression Scale (EPDS) score ≥ 10, and infants were < 12 months. Between 2017 and 2020, 141 mother–infant dyads were randomized to experimental or control groups. Infant ER was measured at baseline (T1) and nine weeks later (T2) using two neurophysiological measures (frontal alpha asymmetry (FAA) and high-frequency heart rate variability (HF-HRV)), and informant-report of infant temperament. Mothers were a mean of 30.8 years old (SD = 4.7), 92.3% were married/ common-law, and infants were a mean of 5.4 months old (SD = 2.9) and 52.1% were male. A statistically significant group-by-time interaction was found to predict change in HF-HRV between T1 and T2 (F(1,68.3) = 4.04, p = .04), but no significant interaction predicted change in FAA or temperament. Results suggest that PHN-delivered group CBT for PPD may lead to adaptive changes in a neurophysiological marker of infant ER, highlighting the importance of early maternal intervention.
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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 | 0.001 |
| 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) | 0.001 | 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".