Dropout and Abstinence Outcomes in a National Text Messaging Smoking Cessation Intervention for Pregnant Women, SmokefreeMOM: Observational Study
Bibliographic record
Abstract
BACKGROUND: Population-level text messaging smoking cessation interventions may reduce racial and ethnic differences in smoking among pregnant women. OBJECTIVE: Our objective was to examine racial and ethnic differences in dropout, response, and abstinence rates among users of a US national, publicly available text messaging cessation intervention targeting pregnant women, SmokefreeMOM. METHODS: Participants were online subscribers to SmokefreeMOM who set a prospective quit date within the 9 months before their due date. We examined demographics, smoking frequency, number of cigarettes smoked per day, and prequit time (up to 14 days of preparation time before quit date) as correlates of response rate and abstinence at 8 time points: quit date, day 7, day 14, day 21, day 28, day 35, day 42 (intervention end), and day 72 (1-month follow-up). We conducted survival analysis of time from quit date to dropout by race and ethnicity. RESULTS: The mean age of the analytic sample of 1288 users was 29.46 (SD 7.11) years. Of these, 65.81% (848/1288) were white, 16.04% (207/1288) were black, 8.86% (114/1288) were Latina, and 9.29% (120/1288) were multiracial, American Indian/Alaska Native, Native Hawaiian Pacific Islander, or other; 82.68% (1065/1288) had some college education or less. Point-prevalence abstinence was 14.51% (157/1082) on quit day, 3.51% (38/1082) at intervention end, and 1.99% (21/1053) at 1-month follow-up. Black users (hazard ratio 0.68, 95% CI 0.51-0.91) and those with a high school degree or less (hazard ratio 0.66, 95% CI 0.49-0.89) or some college education (hazard ratio 0.75, 95% CI 0.57-0.99) were less likely to drop out than whites or users with a bachelor's degree or higher. Response and abstinence rates were similar across race, ethnicity, and education. CONCLUSIONS: Enrollment was low among racial and ethnic minority women but high among less-educated women. Abstinence at intervention end and 1-month follow-up was lower than that in controlled trials of text messaging cessation interventions for pregnant women (range 7%-20%). Increasing the reach, engagement, and effectiveness of SmokefreeMOM, especially among women with high rates of smoking during pregnancy, must be prioritized.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".