Training in Public Health Nutrition: symposium at the 17th International Congress of Nutrition, Vienna
Bibliographic record
Abstract
This session was an important opportunity to debate the concepts surrounding education and training in Public Health Nutrition.Co-chairs Drs Margetts 1 (UK) and Wade (Senegal) focused attention on this theme, highlighting the main purpose that shapes specialist education in Public Health Nutrition is to produce capacity for research, leadership, planning policy and programmes that benefit public health.Brief mention was made of the Nutrition Society's British schemes that register competent individuals and accredit courses that develop competence consistent with registration in Public Health Nutrition 2 .This initiative stimulated a project to develop a European Masters in Public Health Nutrition with European Commission funding; part of a strategy to develop capacity in a range of aspects of European public health 3 .Nearing completion, this project expanded the scope of Public Health Nutrition to explicitly include physical activity, which is implicit to the British definition.In Europe, this commitment is natural and coincides with ongoing British efforts to develop the public health function by developing a career for non-medical specialists.Against this background, there has been muted discussion in Europe about the merits of Public Nutrition rather Public Health Nutrition.We now know that this is also true in Latin America.Solomons 4 reported on a survey among colleagues to find out what Latin American nutritionists call themselves and what their area of core work is.The most popular option was Public Health Nutrition; not Public Nutrition, Community Nutrition, or any other alternative.Overviews of syllabuses for Masters courses from Senegal 5 , Europe 2,3 , and Latin America 4 showed remarkable similarity in conception and scope.Dr Wade acknowledged encouragement from international nutritionists to start the first francophone African Masters in Nutrition.Unsurprisingly, Professor John Waterlow provided some encouragement.Among Waterlow's towering achievements in Nutrition is institution building.Unusually, the MRC Unit agreed to sustain the Tropical Metabolism Research Unit (TMRU) within the University of the West Indies after Waterlow left.TMRU has since had three West Indian directors.TMRU and the London School of Tropical Medicine and Hygiene, where Waterlow was also head of department, have been offering MScs in Nutrition for 30 years.So why not Senegal?This was not
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".