Cigarette use and smoking cessation goals among pregnant women with opioid use disorder
Bibliographic record
Abstract
INTRODUCTION: Cigarette smoking rates among pregnant women with opioid use disorder (OUD), are significantly higher than those found in the general population. METHODS: We conducted a secondary analysis of baseline data from a multisite, randomized clinical trial comparing two different buprenorphine formulations on outcomes during pregnancy. Cigarette use and smoking cessation goals were evaluated with the Fagerström Test for Nicotine Dependence and the Thoughts About Abstinence (TAA) questionnaire respectively. Factors associated with differences in cigarette use and smoking cessation goals were compared. RESULTS: Among 156 participants, 85 (54.5 %) reported that they currently smoked cigarettes. Most participants had a desire to quit smoking (TAA score = 6), but they had low expectations of success (TAA score = 4) and a relatively high perceived difficulty (TAA score = 6.5) of quitting during pregnancy. Among participants who smoked, less than half (45.5 %) had a smoking cessation goal. Participants who had a smoking cessation goal were significantly more likely to have a stronger desire to quit and higher expectations of success in quitting than participants who did not have a goal. CONCLUSIONS: Many pregnant women with OUD would like to quit or reduce smoking during pregnancy. A combination of pharmacologic and non-pharmacologic interventions to reduce or eliminate cigarette use should be incorporated into obstetric and substance use treatment clinical settings. Smoking cessation interventions should be aligned with patients' goals and preferences. TRIAL REGISTRATION: Clinical Trials.govhttp://www. CLINICALTRIALS: gov; Identifier: NCT03918850.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".