Alcohol Screening for Women in their Childbearing Years: What are Health Care Providers doing in Canada?
Bibliographic record
Abstract
Abstract Health care providers (HCPs) have an important role in screening for alcohol use across the lifespan, particularly during the childbearing years, and providing brief intervention to yield optimal outcomes and prevent the potential teratogenic effects of prenatal alcohol exposure. Objective The purpose of this study was to describe the current alcohol screening practices of Canadian HCPs who care for pregnant women and women of childbearing age. Methods An online survey was administered in 2017 with the aim to identify current knowledge, attitudes, practices and beliefs among Canadian HCPs on screening, brief intervention and referral to treatment (SBIRT) for alcohol use for this population. The bilingual survey was disseminated by 4 national professional associations. A total of 634 interprofessional clinicians completed to the survey. Descriptive analysis was completed for the respondent’s profession and their practices related to alcohol SBIRT. Cross-tabulation analyses explored the use of different screening questionnaires. Results Most respondents reported asking about alcohol use; however, there was a low overall use of screening questionnaires for both women of childbearing age and those who are pregnant. Low screening rates may equate to missed opportunities for intervention. Low rates of brief intervention and referral were noted even in circumstances where at-risk drinking was identified, with only 16.4% of respondents intervening when pregnant women reported at-risk alcohol consumption. Conclusion Continued efforts are needed to improve alcohol screening practices among women’s HCPs across Canada. Priority areas for training include: understanding validated alcohol screening questionnaires; incorporating brief intervention into routine care; and developing local referral pathways.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".