Responses to alcohol and pregnancy policy pilot: Midwives’ views about proposals to manage risks associated with prenatal alcohol exposure
Bibliographic record
Abstract
The Responses to Alcohol and Pregnancy Policy (RAPP) Project is a pilot study which seeks to address an evidence gap on midwives’ practice and views on mandatory recording of alcohol use during pregnancy, and transfer of this information to the child health record. The study aims to inform development of UK policy on the risks associated with prenatal alcohol exposure (PAE) and foetal alcohol spectrum disorders (FASD). The study sampled the views of qualified midwives currently working in the UK through an online survey and a small number of stakeholder interviews. Most respondents (82.79%) view recording information about alcohol use during pregnancy as already part of routine antenatal care. 96.9% were in favour of asking about alcohol consumption at the booking appointment, but 55.81% did not support asking questions about alcohol use at every appointment. A high percentage said that mandatory alcohol screening and transfer could have a negative effect on patients (over 80% in each case for feeling judged, guilt and shame), while just over half said they would have a negative effect on their role as a midwife: 52.88% for mandatory alcohol screening; 51.92% for transfer of information. We identified four interrelated themes in the qualitative data: Midwifery as a public health role; Barriers to Relationships, Practical Issues; and Consent and Rights. Our results and discussion highlight a lack of clarity about key concepts within current UK policy proposals. This leaves open the possibility that existing ideas about behaviour in pregnancy, risk and maternal responsibility will shape implementation.
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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 | 0.064 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".