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Record W4409450533 · doi:10.4088/jcp.24m15674

One-Day Online Cognitive Behavioral Therapy–Based Workshops for the Prevention of Postpartum Depression

2025· article· en· W4409450533 on OpenAlexaff
Zoe Boland, N Lloyd, Jaslyn Drage, Jesús Serrano-Lomelin, Peter Bieling, David L. Streiner, Ryan J. Van Lieshout

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsDepression (economics)Postpartum depressionCognitive behavioral therapyPsychotherapistCognitionPsychologyCognitive therapyClinical psychologyPsychiatryMedicinePregnancy

Abstract

fetched live from OpenAlex

Postpartum depression (PPD) affects up to 1 in 5 birthing parents and is associated with more future depressive episodes. We aimed to determine if PPD could be prevented with online 1-day cognitive behavioral therapy (CBT)-based workshops. major depressive disorder (MDD). Participants received the workshop plus treatment as usual (TAU; experimental group) or TAU alone (control group). We assessed MDD diagnosis, levels of PPD symptoms, anxiety, social support, mother-infant relationship, and infant temperament at 1, 2, and 3 months postpartum. The primary outcome was MDD at 3 months postpartum assessed using the Mini-International Neuropsychiatric Interview. <.05) in PPD and anxiety at 2 months postpartum. Eligibility criteria resulted in a sample that did not develop MDD at rates high enough to continue the trial and limited study statistical power. Definitive conclusions regarding the effectiveness of online 1-day workshops for preventing PPD are not possible, but these results may warrant future testing with a higher risk sample. ClinicalTrials.gov identifier: NCT05753176.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.118
GPT teacher head0.481
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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