The Smoking Cessation in Pregnancy Incentives Trial (CPIT): study protocol for a phase III randomised controlled trial
Bibliographic record
Abstract
Abstract Background Eighty percent of UK women have at least one baby, making pregnancy an opportunity to help women to stop smoking before their health is irreparably compromised. Smoking cessation during pregnancy helps protect infants from miscarriage, still birth, low birth weight, asthma, attention deficit disorder and adult cardiovascular disease. UK national guidelines highlight lack of evidence for effectiveness of financial incentives to help pregnant smokers quit. This includes a research recommendation: Within a UK context, are incentives an acceptable, effective and cost-effective way to help pregnant women who smoke to quit? Methods CPIT III is a pragmatic, 39-month, multi-centre, parallel group, individually randomised controlled superiority trial of the effect on smoking status of adding to usual smoking cessation support the offer of up to £400 of financial voucher incentives, compared with usual support alone, to quit smoking during pregnancy. Participants (n = 940) are pregnant smokers (age > 16, <24 weeks pregnant, English speaking), who consent via telephone to take part and are willing to be followed-up in late pregnancy and 6 months after birth. The primary outcome is cotinine/anabasine validated abstinence from smoking in late pregnancy. Secondary outcomes include engagement with cessation services, quit rates at four weeks from agreed quit date and 6 months after birth, and birth weight. Outcomes will be analysed by intention-to-treat, and regression models will be used to compare treatment effects on outcomes. A meta-analysis will include data from the feasibility study in Glasgow. An economic evaluation will assess cost-effectiveness from a UK NHS perspective. Process evaluation using a case study approach will identify opportunities to improve recruitment and learning for future implementation. Research questions: What is the therapeutic efficacy of incentives? Are incentives cost-effective? What are the potential facilitators and barriers to implementing incentives in different parts of the UK? Discussion This phase III trial in Scotland, England and Northern Ireland, follows a successful phase II trial in Glasgow UK. The participating sites have diverse smoking cessation services, that represent most cessation services in the UK and serve demographically varied populations. If found to be acceptable and cost-effective this trial could demonstrate that financial incentives are effective and transferable to most UK cessation services for 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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.091 | 0.018 |
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".