Using<scp>FIGO</scp>Nutrition Checklist counselling in pregnancy: A review to support healthcare professionals
Bibliographic record
Abstract
The period before and during pregnancy is increasingly recognized as an important stage for addressing malnutrition. This can help to reduce the risk of noncommunicable diseases in mothers and passage of risk to their infants. The FIGO Nutrition Checklist is a tool designed to address these issues. The checklist contains questions on specific dietary requirements, body mass index, diet quality, and micronutrients. Through answering these questions, awareness is generated, potential risks are identified, and information is collected that can inform health-promoting conversations between women and their healthcare professionals. The tool can be used across a range of health settings, regions, and life stages. The aim of this review is to summarize nutritional recommendations related to the FIGO Nutrition Checklist to support healthcare providers using it in practice. Included is a selection of global dietary recommendations for each of the components of the checklist and practical insights from countries that have used it. Implementation of the FIGO Nutrition Checklist will help identify potential nutritional deficiencies in women so that they can be addressed by healthcare providers. This has potential longstanding benefits for mothers and their children, across generations.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".