Are Obstetricians Following Best-Practice Guidelines for Addressing Pregnancy Smoking? Results from Northeast Tennessee
Bibliographic record
Abstract
BACKGROUND: In 2000, the American College of Obstetricians/Gynecologists(ACOG) established the 5 A's method of brief smoking cessation counseling (ask, advise, assess, assist, arrange) as a standard component of prenatal care. The purpose of this study was to describe use of the 5 A's in prenatal care in Northeast Tennessee, where pregnancy smoking rates are three times the national average, and to evaluate provider attitudes toward addressing pregnancy smoking. METHOD: Surveys were distributed to all obstetric practices in a6-county area. RESULTS: One-quarter of respondents indicated they always asked pregnant patients about smoking, with two-thirds always giving their pregnant smokers advice to quit. Over half reported always assessing willingness to quit, while one-quarter or fewer always provided quit assistance, or arranged follow up. Over half believed addressing smoking was of significant value. Secondhand smoke was infrequently addressed. Demographics, efficacy, and outcome beliefs predicted use of the 5 A's. CONCLUSIONS: Most obstetric providers in Northeast Tennessee are not following ACOG recommendations for pregnancy smoking. Efforts to address pregnancy smoking and associated adverse pregnancy outcomes in the region should include facilitation of smoking cessation interventions in prenatal care.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".