Public health nutrition capacity: assuring the quality of workforce preparation for scaling up nutrition programmes
Bibliographic record
Abstract
OBJECTIVE: To describe why and how capacity-building systems for scaling up nutrition programmes should be constructed in low- and middle-income countries (LMIC). DESIGN: Position paper with task force recommendations based on literature review and joint experience of global nutrition programmes, public health nutrition (PHN) workforce size, organization, and pre-service and in-service training. SETTING: The review is global but the recommendations are made for LMIC scaling up multisectoral nutrition programmes. SUBJECTS: The multitude of PHN workers, be they in the health, agriculture, education, social welfare, or water and sanitation sector, as well as the community workers who ensure outreach and coverage of nutrition-specific and -sensitive interventions. RESULTS: Overnutrition and undernutrition problems affect at least half of the global population, especially those in LMIC. Programme guidance exists for undernutrition and overnutrition, and priority for scaling up multisectoral programmes for tackling undernutrition in LMIC is growing. Guidance on how to organize and scale up such programmes is scarce however, and estimates of existing PHN workforce numbers - although poor - suggest they are also inadequate. Pre-service nutrition training for a PHN workforce is mostly clinical and/or food science oriented and in-service nutrition training is largely restricted to infant and young child nutrition. CONCLUSIONS: Unless increased priority and funding is given to building capacity for scaling up nutrition programmes in LMIC, maternal and child undernutrition rates are likely to remain high and nutrition-related non-communicable diseases to escalate. A hybrid distance learning model for PHN workforce managers' in-service training is urgently needed in LMIC.
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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 | 0.158 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".