R u a smkn m0m?: Aspects of a Text Messaging Smoking Cessation/Reduction Intervention for Younger Mothers
Bibliographic record
Abstract
Introduction:Women who are younger in age are more likely to smoke during pregnancy and postpartum and tend to have less success with cessation/reduction. There is an unmet need for interventions targeted to pregnant and postpartum young women that provide them with support to quit/reduce long-term into the postpartum period and beyond. Aims:Our study aimed to gain an in-depth understanding of the perspectives of young pregnant and postpartum women of text messaging (TM) as a conduit for smoking cessation/reduction, and to determine the appropriate content, frequency, duration and unique features needed for an effective cessation/reduction TM programme. Methods:Six focus groups and six telephone interviews were convened with 36 pregnant and postpartum women 16–30 years of age. Results:Three main themes were identified: 1) topic areas that women would like TM to focus on; 2) the need for messages to be tailored; and 3) the importance for the programme to take a woman-centered approach. Conclusions:Respondents supported the idea of a TM cessation/reduction intervention and had clear programme recommendations. A personalised, woman-centered TM programme that meets a young woman's unique needs and addresses her concerns through her participation and direction is likely to empower and support her to quit/reduce.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".