Online Public Health Nurse–Delivered Group Cognitive Behavioral Therapy for Postpartum Depression
Bibliographic record
Abstract
Rates of postpartum depression (PPD) increased during the COVID-19 pandemic, further highlighting the need for effective, accessible treatments for PPD. While public health nurses (PHNs) can be trained to help treat PPD, it is not known if they can effectively deliver evidence-based psychotherapies online to those with PPD. Mothers (n = 159) living in Ontario, Canada, with an Edinburgh Postnatal Depression Scale (EPDS) score ≥ 10 and an infant < 12 months of age were randomized to receive a 9-week group cognitive behavioral therapy (CBT) intervention delivered by PHNs over Zoom, between October 2020 and November 2021. Experimental group participants received CBT plus treatment as usual (TAU), and control participants received TAU alone. Participants were assessed at baseline (T1), 9 weeks later (T2), and 6 months after T2 (T3). Primary outcomes were changes in EPDS score and current major depressive disorder (MDD) as measured by the Mini International Neuropsychiatric Interview. Secondary outcomes included worry, social support, the mother-infant relationship, and infant temperament. = 0.44). They were also less likely to meet diagnostic criteria for current MDD compared to control participants (OR = 5.09; 95% CI, 1.18-21.98; number needed to treat [NNT: 3.7]). These improvements remained stable 6 months later (T3). PHNs can be trained to deliver effective online group CBT for PPD to reduce depression and worry and improve aspects of the mother-infant relationship, and they represent an important way to increase access to effective treatment for PPD. ClinicalTrials.gov identifier: NCT04928742.
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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 | 0.002 |
| 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) | 0.005 | 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".