Expectant Parents’ Preferences for Teaching by Texting: Development and Usability Study of SmartMom
Notice bibliographique
Résumé
BACKGROUND: Prenatal education encourages healthy behavioral choices and reduces rates of adverse birth outcomes. The use of mobile health (mHealth) technologies during pregnancy is increasing and changing how pregnant people acquire prenatal education. SmartMom is an evidence-based prenatal education SMS text messaging program that overcomes barriers to prenatal class attendance, including rural or remote location, cost, stigma among participants, lack of instructors, and cessation of classes during the COVID-19 pandemic. OBJECTIVE: We sought to explore perceived information needs and preferences for the content and structure of prenatal education mHealth programs among persons enrolled in or eligible to enroll in SmartMom. METHODS: This was a qualitative focus group study conducted as part of a development and usability study of the SmartMom program. Participants were older than 19 years of age, Canadian residents, fluent in English, and either currently pregnant or pregnant within the last year. We asked open-ended questions about information-seeking behaviors during pregnancy, the nature of the information that participants were seeking, how they wanted to receive information, and if SmartMom was meeting these needs. Focus groups took place via videoconference technology (Zoom) between August and December 2020. We used reflexive thematic analysis to identify themes that emerged from the data and the constant comparison method to compare initial coding to emerging themes. RESULTS: We conducted 6 semistructured focus groups with 16 participants. All participants reported living with a partner and owning a cell phone. The majority (n=13, 81%) used at least 1 app for prenatal education. Our analysis revealed that "having reliable information is the most important thing" (theme 1); pregnant people value inclusive, local, and strength-based information (theme 2); and SMS text messages are a simple, easy, and timely modality ("It was nice to have that [information] fed to you"; theme 3). Participants perceived that SmartMom SMS text messages met their needs for prenatal education and were more convenient than using apps. SmartMom's opt-in supplemental message streams, which allowed users to tailor the program to their needs, were viewed favorably. Participants also identified that prenatal education programs were not meeting the needs of diverse populations, such as Indigenous people and LGBTQIA2S+ (lesbian, gay, bisexual, transgender, queer and/or questioning, intersex, asexual, Two-Spirit plus) communities. CONCLUSIONS: The shift toward digital prenatal education, accelerated by the COVID-19 pandemic, has resulted in a plethora of web- or mobile technology-based programs, but few of these have been evaluated. Participants in our focus groups revealed concerns about the reliability and comprehensiveness of digital resources for prenatal education. The SmartMom SMS text messaging program was viewed as being evidence-based, providing comprehensive content without searching, and permitting tailoring to individual needs through opt-in message streams. Prenatal education must also meet the needs of diverse populations.
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 | 0,010 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».