Doula-Delivered Cognitive Behavioral Training and Cardiovascular Health Intervention for Birthing Individuals in a Low-Income New York City Population: Protocol for a Living Healthy for Moms Randomized Type I Hybrid Effectiveness-Implementation Trial
Bibliographic record
Abstract
Background In the United States, mental health complications and cardiovascular events are the 2 leading causes of death for birthing parents in the year following delivery. Most of these deaths are preventable, with Black and Latinx individuals experiencing higher rates of these postpartum complications. Current postpartum care has not reduced these disparities. Objective This randomized controlled trial aims to train nonmedical professionals in a novel intervention to prevent postpartum depressive symptoms and improve cardiovascular health following childbirth in a low-income New York City (NYC) birthing population. Methods We aim to recruit 600 birthing individuals over 3 sites across NYC. After screening and consent, participants will be randomized to the Living Healthy for Moms intervention (doula-delivered cognitive behavioral therapy and cardiovascular behavioral health intervention) or attention control (a variation of standard postpartum doula care). Daily telephone contacts for the first 7 days immediately after hospital discharge are followed by 12 doula-led video sessions conducted over 6 months. The primary outcomes are postpartum depressive symptoms and cardiovascular health, with secondary outcomes of psychosocial status, health behaviors, health care use, and patient satisfaction. All outcomes are measured via REDCap (Research Electronic Data Capture; Vanderbilt University) surveys administered at baseline, 2 weeks, 6 weeks, 3 months, and 6 months post discharge. Physiological measurements of glycated hemoglobin (hemoglobin A1c), lipids, and blood pressure will be collected at baseline, 3 months, and 6 months. Doulas, hospital staff, and birthing individuals will be recruited to evaluate the implementation of the intervention following the conclusion of recruitment at each site. A mixed methods triangulated approach will be used, including electronic health record data extraction, web-based surveys, key informant interviews, and focus groups. Results Recruitment and data collection began at the first site in Brooklyn on January 23, 2025. Data collection for participants from each of the 3 recruitment sites will be concluded by November 30, 2027, February 29, 2028, and August 31, 2029, respectively, for Brooklyn, Queens, and upper Manhattan. Thus, all data for the total expected 600 participants will be collected by the end of the grant year 6, August 31, 2029. Trial results will not be analyzed until grant year 7, beginning September 1, 2029. Data will be analyzed on an intention-to-treat basis by study team members blinded to participant conditions. Conclusions This hybrid type 1 effectiveness-implementation randomized controlled trial will test a novel nonspecialist intervention to prevent mental health and cardiovascular health complications of childbirth, culturally adapted to our local population in NYC. We expect that our findings will contribute to knowledge on the effectiveness and implementation of nonspecialist postpartum interventions in low-resourced settings and the expansion of doula care in the post partum. Trial Registration ClinicalTrials.gov NCT06666400; https://clinicaltrials.gov/study/NCT06666400 International Registered Report Identifier (IRRID) DERR1-10.2196/76871
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".