Screening, Brief Intervention, and Referral to Treatment for Prenatal Alcohol Use and Cigarette Smoking: A Survey of Academic and Community Health Care Providers
Bibliographic record
Abstract
OBJECTIVES: Prenatal alcohol and cigarette smoking are associated with numerous adverse pregnancy outcomes. Screening, Brief Intervention, and Referral to Treatment (SBIRT) represents a standardized approach; however, implementation in routine pregnancy care remains a challenge. The purpose of the study was to determine current practices, barriers to implementation, and education needs of healthcare providers utilizing SBIRT to address prenatal alcohol and cigarette smoking. METHODS: We conducted a survey of 118 providers including family physicians, midwives, and obstetricians practicing at 2 Toronto hospitals: community-based teaching site and fully affiliated academic health sciences center. RESULTS: The response rate was 79%. Almost all providers reported screening every pregnant woman for alcohol and smoking status. Brief intervention was offered by fewer providers. Education and supportive counseling were reported by a higher percentage of providers for prenatal cigarette smoking in comparison to alcohol use. Furthermore, up to 60% referred pregnant women to treatment programs for alcohol and cigarette smoking. A significantly higher number of community-based providers reported referring pregnant women to addiction treatment programs. Barriers to interventions included a perceived lack of appropriate resources, training, and clinical pathways. CONCLUSION: Healthcare providers report universal screening for prenatal alcohol and cigarette smoking; however, brief intervention and referral to treatment are more limited practices. There is a need for education of all providers regarding effective brief counseling strategies and referral to appropriate treatment resources. Development of clinical care pathways may also increase adoption of all components of SBIRT for prenatal alcohol use and cigarette smoking.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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) | 0.002 | 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".