Evaluation of a Text Messaging Intervention to Support Self-Management of Diabetes During Pregnancy Among Low-Income, Minority Women: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Given the growing burden of diabetes in underserved communities and the complexity of diabetes self-management during pregnancy, the development of interventions to support low-income pregnant women with diabetes is urgently needed. OBJECTIVE: This study aims to develop and pilot test a theory-driven curriculum of SMS text messaging for diabetes support and education during pregnancy. METHODS: This was a prospective pilot investigation of a novel SMS text messaging intervention offered to pregnant women with pregestational or gestational diabetes mellitus and publicly funded prenatal care. Prior work yielded a conceptual model of diabetes self-management barriers and support factors in this population, which was used to guide curriculum development along with health behavior theories. Participants received three supportive or educational one-way text messages per week during pregnancy. In-depth semistructured interviews were performed at study exit to solicit feedback on the program. Narrative data were analyzed using the constant comparative technique to identify themes and subthemes. RESULTS: Participants (N=31 enrolled and n=26 completed both interviews) consistently reported that SMS text messaging provided enhanced motivation for diabetes self-care, reduced diabetes-related social isolation, increased perceived diabetes-associated knowledge, enhanced comfort with the health care team, and reduced logistical burdens of diabetes during pregnancy. Participants requested enhanced interactive and customizable features in future intervention iterations. CONCLUSIONS: Pregnant women with diabetes who were enrolled in this pilot study of an SMS text messaging curriculum for diabetes support described enhanced motivation, knowledge, and comfort with diabetes self-care activities as a result of the health education intervention. The next steps include enriching the interactive features of the intervention and investigating the effect of the intervention on perinatal outcomes.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".