World Health Organization and knowledge translation in maternal, newborn, child and adolescent health and nutrition
Bibliographic record
Abstract
The World Health Organization (WHO) has a mandate to promote maternal and child health and welfare through support to governments in the form of technical assistance, standards, epidemiological and statistical services, promoting teaching and training of healthcare professionals and providing direct aid in emergencies. The Strategic and Technical Advisory Group of Experts (STAGE) for maternal, newborn, child and adolescent health and nutrition (MNCAHN) was established in 2020 to advise the Director-General of WHO on issues relating to MNCAHN. STAGE comprises individuals from multiple low-income and middle-income and high-income countries, has representatives from many professional disciplines and with diverse experience and interests.Progress in MNCAHN requires improvements in quality of services, equity of access and the evolution of services as technical guidance, community needs and epidemiology changes. Knowledge translation of WHO guidance and other guidelines is an important part of this. Countries need effective and responsive structures for adaptation and implementation of evidence-based interventions, strategies to improve guideline uptake, education and training and mechanisms to monitor quality and safety. This paper summarises STAGE's recommendations on how to improve knowledge translation in MNCAHN. They include support for national and regional technical advisory groups and subnational committees that coordinate maternal and child health; support for national plans for MNCAHN and their implementation and monitoring; the production of a small number of consolidated MNCAHN guidelines to promote integrated and holistic care; education and quality improvement strategies to support guidelines uptake; monitoring of gaps in knowledge translation and operational research in MNCAHN.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".