Alcohol and Drug Screening of Newborns: Would Women Consent?
Bibliographic record
Abstract
OBJECTIVES: To examine the conditions under which mothers would consent to alcohol and drug screening of their infants, and to identify predictors of screening consent. METHODS: A cross-sectional survey was administered in person by trained research assistants on the postpartum units of three hospitals in a large Canadian urban centre over four months. The survey was administered to 1509 mothers (78.4% of those eligible) who were fluent in English and had given birth within the preceding 48 hours. RESULTS: Mothers indicated that they would consent to screening of their newborn (1369/1460, 93.8%), and thought all mothers should consent if infants at risk would be more likely to receive effective treatment (1440/1476, 97.6%). Respondents believed that they would consent to screening if they were provided the following information: what would happen if the infant sample was positive for prenatal exposure (1431/1476, 97%); who would have access to the information (1377/1476, 93.4%); how effective medical care would be for the child (1435/1476, 97.4%); and the likelihood that a baby with a positive screen would have a problem (1444/1476, 98.1%). Self-reported alcohol use did not decrease willingness to consent. In a multivariate model, belief that universal screening would not make women feel discriminated against was a significant predictor of consent (adjusted OR 5.9; 95% CI 3.3-10.6). CONCLUSION: Mothers would support a universal newborn alcohol and drug screening program if there was evidence that screening could lead to effective treatment for the mother and baby, and if appropriate resources were available.
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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".