Smoking behaviors and abstinence in low-income pregnant women
Bibliographic record
Abstract
Background : Despite efforts to educate individuals about the hazards of smoking, pregnant women continue to smoke. In the literature, there is less evidence about successful abstinence strategies for low-income women. The purpose of this pilot study was to assess smoking behaviors and factors that support smoking abstinence in low-income pregnant women. Methods : Using a longitudinal design, quantitative and qualitative data were collected from pregnant women at a low-income community prenatal clinic. Based on the Transtheoretical model, all subjects received information about the harmful effects of smoking and secondhand exposure, while current smokers were given a “quit kit” and contacted up to one year post-delivery to evaluate smoking behaviors. Results : All subjects (N = 135) ranged in age from 18 to 41; 75% were not married; 78% had household incomes < $30,000; and the majority were African American (40%). Fifty-five (40.7%) never smoked while 77(57%) had a smoking history, of these 18(23%) were spontaneous quitters. Data indicated that 36% reported smoking during pregnancy, with the majority in pre-contemplation. After one year, 18% of current smokers quit. Conclusions : Without a specific plan, the majority were unable to successfully abstain. Rate of abstinence may have been further influenced because subjects began smoking at an early age and were unsuccessful at previous quit attempts. Providers must continue to educate pregnant women but also evaluate strategies that require few provider visits, are cost effective, focus on relapse prevention, and can successfully influence smoking abstinence in low-income pregnant women.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".