Preventing neural tube defects with folic acid: Nearly 20 years on, the majority of women remain unprotected
Bibliographic record
Abstract
Many countries, including the USA and Canada, have implemented fortification of foods with folic acid, however the British Government only issues advice that supplements should be taken before and after conceiving. In this study, information was collected from women attending antenatal clinics to understand current levels of compliance with health advice and to investigate what is driving womens' behaviour. Eighty-nine percent of women consumed supplements but only 31% took folic acid prior to conceiving. Hence, the vast majority are starting too late to prevent neural tube defects (NTDs). Educational achievement, income and marital status emerged as the most significant factors influencing non-compliance. GPs and midwives were the main catalyst for women starting folic acid, however, 81% of these women started post-conception. When asked why they took folic acid, the majority of women did not mention the association with NTDs. Forty-one percent of women who did not take the supplements at all were unaware that it was recommended that they should. Fortification of UK food products offers a major public health opportunity. In the absence of fortification, gaps in the public health message need to be addressed. GPs and midwives cannot be relied upon alone to educate these women.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".