Preventing Postpartum Smoking Relapse Among Inner City Women: Development of a Theory-Based and Evidence-Guided Text Messaging Intervention
Bibliographic record
Abstract
BACKGROUND: Underserved women are at high risk for smoking relapse after childbirth due to their unique socioeconomic and postpartum stressors and barriers. Mobile text messaging technology allows delivery of relapse prevention programs targeted to their personal needs over time. OBJECTIVE: To describe the development of a social-cognitive theory-based and evidence-guided text messaging intervention for preventing postpartum smoking relapse among inner city women. METHODS: Guided by the cognitive-social health information processing framework, user-centered design, and health communication best practices, the intervention was developed through a systematic process that included needs assessment, followed by an iterative cycling through message drafting, health literacy evaluation and rewriting, review by target community members and a scientific advisory panel, and message revision, concluding with usability testing. RESULTS: All message content was theory-grounded, derived by needs assessment analysis and evidence-based materials, reviewed and revised by the target population, health literacy experts, and scientific advisors. The final program, "Txt2Commit," was developed as a fully automated system, designed to deliver 3 proactive messages per day for a 1-month postpartum smoking relapse intervention, with crave and lapse user-initiated message functions available when needed. CONCLUSIONS: The developmental process suggests that the application of theory and best practices in the design of text messaging smoking cessation interventions is not only feasible but necessary for ensuring that the interventions are evidence based and user-centered.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".