Breastfeeding Rights of Multiple Birth Families and Guidelines for Health Professionals
Bibliographic record
Abstract
Increasing numbers of women wish to breastfeed their multiple birth children. Breastfeeding of preterm and fullterm multiple birth infants is complex and demanding for the families and presents distinct challenges for health professionals. Families require sustained assistance from health care providers who are encouraging, knowledgeable, skilled, and committed to the breastfeeding of multiple birth children. Seven breastfeeding rights of multiple birth families are presented for the continuum of pregnancy to early childhood and are in accordance with the Declaration of Rights and Statement of Needs of Twins and Higher Order Multiples (Council of Multiple Birth Organizations of the International Society for Twin Studies, 1995). Guidelines for each of the rights have been developed to assist health professionals provide “best practices” in community and hospital settings. The guidelines are based on the existing body of breastfeeding of multiples' research, empirical findings, and consultations with parents and care providers with experience and/or expertise in breastfeeding multiples. The rights and guidelines suggest direction for providing assistance, implementing programs and services, conducting research, and evaluating the effectiveness of multiples-specific breastfeeding care during the prenatal, infancy, and toddlerhood periods.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".