Exposure to and Engagement With Digital Psychoeducational Content and Community Related to Maternal Mental Health by Perinatal Persons and Mothers: Protocol for a Web-Based Survey With Optional Follow-Up (Preprint)
Bibliographic record
Abstract
BACKGROUND Leveraging digital platforms may be an effective strategy for connecting perinatal persons and mothers with evidence-based information and support related to maternal mental health and peers. Momwell is a mom-centered model of care that provides psychoeducational content through several digital platforms, including social media, a podcast, and a blog. The aims of this project were to describe how perinatal persons and mothers engage with Momwell’s psychoeducational content and community; describe the perceived benefits of exposure to and engagement with content and community; examine associations between engagement with digital psychoeducational content and maternal mental health, parenting attitudes, and interparental relationships; and examine changes in mental health and parenting attitudes and concurrent engagement with Momwell’s digital psychoeducational content and community over 2 to 3 months. OBJECTIVE This paper aims to describe the design of a study of perinatal persons and mothers who are exposed to or engage with Momwell’s psychoeducational content and community and describe sample characteristics. METHODS Adults who engaged with Momwell on any of their digital platforms were recruited to complete a web-based survey in July 2023 to September 2023. Participants completed either a longer or shorter survey. Participants who provided permission to be recontacted were invited to complete a second survey 2 to 3 months later. The surveys included validated psychological measures, study-specific quantitative questions, and open-ended questions that assessed participant demographics, exposure to and engagement with Momwell’s psychoeducational content and community, maternal mental health, parenting relationships, parenting self-efficacy, and additional psychosocial and health measures. We outline planned analyses to achieve the aims of the project. RESULTS Data collection occurred from July 2023 to September 2023 (N=584). A subset of participants completed the optional second survey in October 2023 to December 2023 (N=246). Participants were >99% mothers (582/584, 99.7%); 45.5% (266/584) perinatal (59/584, 10.1% pregnant; 210/584, 36% post partum); and, on average, aged 32.4 (SD 3.9) years. In total, 59.1% (345/584) were from the United States, 35.6% (208/584) were from Canada, and 5.3% (31/584) were from other countries. The vast majority (552/584, 94.5%) followed Momwell on Instagram, 44.2% (258/584) listened to the Momwell podcast, and 41.1% (240/584) received their newsletter. Most participants had been exposed to Momwell’s psychoeducational content for at least 6 months across the different platforms (range 16/36, 44% on TikTok to 480/552, 87% on Instagram). CONCLUSIONS Data from this study will provide insights into how pregnant persons and mothers use digital psychoeducational content and peer communities to support their mental health throughout the perinatal period and into the early years of motherhood. Leveraging digital platforms to disseminate evidence-based digital psychoeducational content related to maternal mental health and connect peers has the potential to change how we care for perinatal persons and mothers. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/64075
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.019 |
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".