Knowledge, beliefs, behaviours, and decision making associated with alcohol consumption during pregnancy in an urban prenatal population
Bibliographic record
Abstract
Despite the age old warnings against the use of alcohol during pregnancy, and the abundance of scientific research that links Fetal Alcohol Syndrome (FAS) and Fetal Alcohol Effects (FAE) with prenatal maternal alcohol ingestion, FAS/FAE remains one of the leading causes of birth defects and mental retardation. It is critical to explore the reasons why women may choose to drink alcohol during their pregnancies to identify areas which may direct interventions and future research. A descriptive cross-sectional survey was completed using Beeker's Health Belief Model (1974) as a guide to exploring the phenomena of alcohol consumption during pregnancy. A researcher designed self-reporting questionnaire that incorporated previously used tools and open-ended questions was used to gather information from the 117 pregnant study participants regarding their knowledge, beliefs, behaviours, and decision making related to alcohol consumption during pregnancy. A small number of study participants continued to drink duringtheir pregnancy. They tended to be older, Caucasian, more educated, in a higher family income bracket, and with slightly lower knowledge scores. In general, the study sample possessed a high knowledge level and high levels of perceived susceptibility and severity to FAS/FAE. The most common benefit cited by participants for abstaining from alcohol during pregnancy was for health reasons, especially related to the baby. The most common barrier to abstaining was related to alcohol being an enjoyable part of the woman's lifestyle. Recommendations are made for health care education, practice, future research, and refinement of the research questionnaire.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".