PROTOCOL: Effects of preconception care and periconception interventions on maternal nutritional status and birth outcomes in low‐ and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND | The problem, condition or issueInterest in preconception health for maximising gains for mothers and babies started with the release of the seminal report from Centre for Disease Control (Johnson et al., 2006).Further, in 2011, the World Health Organisation (WHO) convened a meeting of experts where there was an overwhelming agreement on the potential for preconception care to have a positive impact on maternal and child health outcomes (World Health Organisation, 2013).Since then there is growing awareness of the importance of the preconception period and efforts have been made to increase awareness and promote reproductive health from adolescents onwards.Preconception care is important for healthy maternal, birth, and neonatal health outcomes (Dean et al., 2013).Optimising a woman's health before planning and conceiving pregnancy is increasingly recognised as an important strategy to enhance maternal and child health (Dean, Imam, Lassi and Bhutta, 2013).Preconception period is an ideal time to introduce interventions relating to nutrition and other lifestyle factors to promote health and for ensuring good pregnancy preparedness.Since 99% of all maternal and newborn deaths occur in low-and middle-income countries (LMICs; World Health Organisation, 2017), early start of preconception care particularly for girls living in LMICs is very crucial.At present, policies and guidelines on preconception care are scarce and the care starts when the women becomes pregnant and extends to childbirth and postnatal period (for mothers and babies).There is a clear gap in -------------------------------------------------
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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 | 0.040 | 0.055 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.024 | 0.017 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.124 | 0.012 |
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".