Expectant Parents’ Preferences for Teaching by Texting: Development and Usability Study of SmartMom (Preprint)
Bibliographic record
Abstract
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.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".