Estimation of alcohol content of wine, beer and spirits to evaluate exposure risk in pregnancy: Pilot study using a questionnaire and pouring task in England
Bibliographic record
Abstract
Mukherjee, R., Wray, E., Curfs, L., & Hollins, S. (2013). Estimation of alcohol content of wine, beer and spirits to evaluate exposure risk in pregnancy: Pilot study using a questionnaire and pouring task in England. The International Journal Of Alcohol And Drug Research, 2(3), 71-78. doi:10.7895/ijadr.v2i3.78 (http://dx.doi.org/10.7895/ijadr.v2i3.78)Aims: Research has shown varying results regarding safe consumption levels of alcohol during pregnancy. We argued in 2005 that an individual’s inability to accurately predict her alcohol consumption may be one factor influencing risk. In order to re-evaluate within the England, this study sought to assess the current knowledge of the public and of healthcare practitioners.Design: Both alcohol-knowledge questionnaires and pouring tasks were conducted using standardised ethical-committee-approved methods.Settings: Different sites across England, including Surrey, London, Oxford and Wigan, where FASD support groups are based.Participants: Health professionals and the general public, self-selecting in response to advertisement.Measurements: Frequency data and categorical data was collected and analysed using SPSS version 18.Findings: In total, 1,265 questionnaires were completed (688 public and 577 professionals). One hundred-forty people completed the pouring task. People’s ability to calculate accurately from strength and volume was within 20% of the accurate figure for units, although with a wide range.Conclusions: These findings support the hypothesis that when pouring their own drinks, individuals are poor at estimating each drink’s alcohol content. This has implications for public health strategies. Glass size and the level of alcohol concentration have different implications in different countries. For those drinking during pregnancy, however, the message that “no exposure is no risk” remains true.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".