Developing Culturally Sensitive mHealth Apps for Caribbean Immigrant Women to Use During Pregnancy: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: A valuable addition to the mobile health (mHealth) space is an exploration of the context of minorities in developed countries. The transition period postmigration, culture, and socioeconomic uniqueness of migratory groups can shed light on the problems with existing prenatal mHealth apps. OBJECTIVE: The objectives of this study were to (1) use the theoretical concept of pregnancy ecology to understand the emotional, physical, information, and social challenges affecting low-income Caribbean immigrant women's prenatal well-being practices and (2) develop a deep understanding of challenges worthy of consideration in mHealth design for these women. METHODS: This qualitative interpretive approach using analytical induction presents the findings of 3 focus group sessions with 12 Caribbean immigrant women living in South Florida in the United States. The study took place from April to September 2015. RESULTS: The participants revealed problematic tiers and support needs within the pregnancy ecology including emotional stressors caused by family separation, physical challenges, information gaps, and longing for social support. CONCLUSIONS: mHealth interventions for low-income Caribbean immigrant women must be designed beyond the conventional way of focusing on the events surrounding the unborn child. It can be tailored to the needs of the expecting mother. Pregnancy information should be customized on the basis of the variability of lifestyle, cultural practices, socioeconomic status, and social ties while still being able to deliver appropriate guidelines and clear cultural misconceptions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".