Social Interaction Needs and Entertainment Approaches to Pregnancy Well-Being in mHealth Technology Design for Low-Income Transmigrant Women: Qualitative Codesign Study
Bibliographic record
Abstract
BACKGROUND: Low-income Caribbean transmigrant women face unique health challenges during pregnancy that set forth multidimensional implications for the design of mobile health (mHealth). Acknowledgment of the unique health needs of low-income Caribbean immigrant women in the United States and what that entails regarding technology design remains rarely examined in the literature of mHealth technologies. OBJECTIVE: The goal of this study was to reveal the needs and gaps in mHealth interventions for pregnant immigrant women not yet realized in this field. These understandings reveal design opportunities for mHealth. METHODS: The use of the qualitative participatory action research approach of codesign workshops in this study resulted in design solutions by the participants after reflecting on their earlier focus group discussions. The highlights are not the resulting designs per se but rather the inferences derived from the researcher reflecting on these designs. RESULTS: The designs exposed two themes relevant to this paper. First, the participants desired the inclusion and rebuilding of social and organizational relationships in mHealth. The resulting designs formulate an understanding of the women's health-related social support needs and how technology can facilitate them. Second, the participants wanted entertainment with an element of social participation incorporated in mHealth pregnancy management interventions. This brings attention to the role entertainment can add to the impact mHealth can deliver for pregnancy well-being. CONCLUSIONS: The study concluded with an examination of social and entertainment design implications that reveal pregnant immigrant women's virtual health-related sharing habits, choice of sharing interaction scenarios during pregnancy (eg, local, long distance, one-way, two-way, and many-many), and choice of sharing media (eg, text, voice, and video). Additionally, the study revealed exclusions to social sharing capabilities in health technologies for these women.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".