The effectiveness of adapted, best practice guidelines for smoking cessation counseling with disadvantaged, pregnant smokers attending public sector antenatal clinics in Cape Town, South Africa
Bibliographic record
Abstract
AIM AND OBJECTIVES: To evaluate the effect of a smoking cessation intervention, based on best practice guidelines on the quit rates of disadvantaged, pregnant women in Cape Town, South Africa. DESIGN: Quasi-experimental using a natural history cohort as a control group, consisting of women attending antenatal care in 2006 and an intervention cohort, attending the same clinics a year later. SETTING: Four, public sector antenatal clinics in Cape Town staffed and managed by midwives. POPULATION: Pregnant women of low socio-economic status. METHODS: The natural history cohort received usual care, whilst the intervention cohort was offered self-help quit materials in the context of brief counseling by midwives and peer counselors. Smoking behavior was measured in early, mid and late pregnancy. The equivalence of the groups in terms of smoking profile, self-reported smoking and demographic variables was assessed at baseline. MAIN OUTCOME MEASURES: Quit rates measured by urinary cotinine towards the end of pregnancy (36-39 weeks gestation). RESULTS: The two cohorts were comparable at baseline. The difference in quit rates between the two cohorts in late pregnancy was 5.3% (95% CI: 3.2-7.4%, p < 0.0001) in an intention to treat analysis. There was also a significant difference in reduction of smoking of 11.8% (95% CI: 5.0-18.4%, p = 0.0006). CONCLUSION: A smoking cessation intervention based on best practice guidelines was effective among high risk, pregnant smokers in South Africa.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".