Development and pilot evaluation of a pregnancy-specific mobile health tool: a qualitative investigation of SmartMoms Canada
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Bibliographic record
Abstract
BACKGROUND: Mobile technology is ubiquitous. Women of childbearing age have embraced health information technology for pregnancy-related counsel as prenatal care provider communication is increasingly scarce and brief. Pregnant women and new mothers place high value in the use of online sources to support their pregnancy information needs. In Canada, over 300,000 women are pregnant annually, with approximately 60% exceeding evidence-based weight gain recommendations. Mobile health (mHealth) tools, such as mobile applications (app), have the potential to reduce excessive gestational weight gain, offering pregnant women trustworthy guidance, ultimately improving the health outcomes of mothers and infants. Therefore, the primary aim of this study was to implement a qualitative, descriptive research design to assess the receptiveness, functionality, and future prospective of the SmartMoms Canada mHealth app. METHODS: Two focus groups (n = 13) involving both currently pregnant and recently postpartum women were organized on the same day. Focus groups were transcribed verbatim and thematic analysis was undertaken using manual coding and NVivo software. Participants who took part in the focus groups (n = 13) and those who could not attend (n = 4) were asked to complete a Likert-scale survey. All survey responses (n = 17) were analyzed using simple tabulation and percentage analysis. RESULTS: Participants were technologically proficient and interacted with several mHealth tools prior to testing the SmartMoms Canada app. Six major themes emerged from thematic analysis: knowledge of pregnancy-specific mHealth services, knowledge and attitudes of weight gain guidelines, weight tracking, strengths of the app, critique and lastly, future suggestions for the app. CONCLUSIONS: Our thematic analysis found that women positively viewed the future potential of our app and offered constructive feedback to improve the next version. Participants sought more personalization and enhanced app interactivity, along with promotion of overall maternal health including nutrition and mental health, in addition to weight tracking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it