Establishing content for a digital educational support group for new adolescent mothers in the Dominican Republic: a user-centered design approach
Bibliographic record
Abstract
BACKGROUND: As digital interventions to improve health become widespread globally, it is critical to include target end-users in their design. This can help ensure interventions are maximally beneficial among intended populations. OBJECTIVES: To generate the content of a digital educational support group, administered through WhatsApp, for new adolescent mothers and establish participants' cellular access and WhatsApp use. PARTICIPANTS: Adolescent mothers with new babies. METHODS: We completed a two-phase user-centered design process. In phase I design sessions, participants discussed their postpartum experiences and completed an activity to elucidate their health and wellbeing information needs. In phase II sessions, participants individually identified which health information topics were important to them, then all topics were prioritized as a group. Phase II participants also completed a brief survey on cell phone access and WhatsApp use. RESULTS: Phase I included 24 participants, 21 of whom completed phase II. Priority health and wellbeing information topics in the postpartum period were identified as: child growth and development, understanding your baby, common childhood illnesses, breastfeeding, childhood nutrition, family planning, and self-care. Of phase II participants, 45% had cellular phone access and none had a data plan. Cellular service was inconsistently obtained with data packages or Wi-Fi. 30% of participants had no experience using WhatsApp. CONCLUSIONS: Participants identified numerous health information needs, which will serve as the content for our planned digital support group and provides valuable insight for health care providers globally. Less than half of participants had consistent cellular phone access, and none had reliable access to cellular service.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".