Bibliographic record
Abstract
PURPOSE OF REVIEW: This study reviews what we know about preconception care, its definition, goals, and content; the science behind the recommended interventions; opportunities for implementing preconception care; and the challenges facing its implementation. RECENT FINDINGS: There is solid scientific evidence that many interventions will improve pregnancy outcomes if delivered before pregnancy or early in pregnancy. Experts continue to explore the most effective means for implementing preconception care, taking into consideration issues related to policy, finance, public health practice, research/surveillance, and consumer and provider education. SUMMARY: Over the past 4 years, there has been renewed interest and a great emphasis on preconception health and healthcare as alternative and additional approaches to counter the persistent increasing incidence in adverse pregnancy outcomes in the United States. Following the publication of the 'Recommendations to Improve Preconception Health and Healthcare' in 2006, many state and local health departments initiated programs to implement the recommendations. Several countries such as Canada, Belgium, and the Netherlands have also started to implement preconception care programs. There are many opportunities for promoting preconception health and providing preconception care; however, making preconception care a standard practice continues to face many barriers.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".