Bibliographic record
Abstract
Background: Approximately 15% of women in Tennessee continue to smoke during pregnancy despite known health risks to themselves and their unborn babies.The use of evidence based interventions is essential to assist pregnant women who smoke in their cessation attempts and in the reduction of adverse effects of smoking during pregnancy.Objective: This scholarly project aims to determine if the use of motivational text messages and a reactive smoking cessation quit line influence smoking behaviors (motivation to quit, dependence on nicotine and number of cigarettes smoked per day) in a group of pregnant women who smoke in Tennessee. Methods:The study sample consisted of pregnant women in Tennessee who self-reported smoking.Participants were enrolled to receive motivational text messages from Smoke-Free Mom, and were given the contact information for the Tennessee Tobacco Quit line.Baseline motivation to quit was obtained with the Motivation to Stop Scale, dependence on nicotine was measured with the Autonomy over Smoking Scale, and self-reported cigarettes smoked per day was obtained.Utilization of the interventions was assessed during the study, and post-intervention motivation to quit score, dependence on nicotine score, and self-reported cigarettes smoked per day were obtained.Results: 43 participants (87%) completed post-test questionnaires.Wilcoxan Signed Ranks test demonstrated an overall increase in self-reported motivation to quit, decrease in dependence, and cigarettes smoked per day.Linear regression demonstrated a correlation between utilization of the text messages and decrease in dependence and an increase in motivation to quit, however, the effect was small.No significant relationship was found between utilization of text messages and cigarettes smoked per day.This study provided inconclusive results supporting the benefit of motivational text messages and a reactive quit line in pregnant women who smoke. Conclusion:High levels of intrinsic motivation present in pregnancy can influence changes in health behaviors irrespective of intervention.Health care providers are encouraged to assess smoking status in all pregnant patients, and to provide smoking cessation counseling to all patients who self-report as a smoker.Further studies are warranted to determine efficacy of motivational text messages and smoking cessation quit lines in pregnant women.Approximately 8.4% of pregnant women in the United States continue to smoke during pregnancy despite warnings about health risks to themselves and their unborn babies [1].The use of evidence-based interventions is essential to assist pregnant women who smoke in their cessation attempts, while reducing the adverse effects of smoking during pregnancy to mother and baby [2].Healthcare providers have a unique opportunity to deliver interventions to assist pregnant women in smoking cessation efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".