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
Notice bibliographique
Résumé
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 IDENTIFIER (IRRID): DERR1-10.2196/64075.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,057 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».