A Health App for Evidence-Based Postpartum Information: Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: After childbirth, women undergo substantial physical and emotional changes. Therefore, it is important to provide them with information that helps them identify what is expected during this stage, as well as signs and symptoms that indicate complications after they have been discharged from the hospital. OBJECTIVE: This study aimed to develop a health app-Towards Motherhood-that provides evidence-based information about the postpartum period and evaluate the usability of the app with the target population. METHODS: This was a validation study involving 80 participants, including 24 professionals from the obstetric health field, 15 professionals from the technology field, and 41 postpartum women. The app was developed using React Native technology. Health professionals evaluated the app's content using the Content Validity Index, technology professionals completed a validated evaluation to assess the appearance of the app, and postpartum women completed the System Usability Scale (SUS) to measure the usability of the app. RESULTS: The measurement of content validity using a Likert scale obtained an approval score of 99%. Regarding the app's appearance, 92% of responses were positive, reflecting favorable approval. The SUS usability score was 86.2, which represents excellent acceptance. CONCLUSIONS: The Towards Motherhood mobile app is a valid tool for promoting self-care during the postpartum period. The app's evidence-based information, user-friendly design, and high usability make it an essential resource for women during this critical stage of their live.
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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 | 0.039 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".