Educational training of nutritionists in Public Health Nutrition: A systematic review
Bibliographic record
Abstract
ABSTRACT The present systematic review aimed to synthesize the findings of studies in the literature on the educational training of nutritionists in Public Health. The study was conducted by searching international databases (PubMed, Science Direct, Lilacs, PsycINFO, Scopus, and Web of Science), as well as in the gray literature. The steps of systematic search followed the recommendations of the Preferred Reporing Itens for Systematic Reviews and Meta-Analysis. Eligibility criteria included studies on the Public Health training in Nutrition Undergraduate courses, whose subjects were students, educators, and professionals who practice in the field of Nutrition. A total of 633 articles were identified, of which, 66 were eligible for reading and analysis of the full text and 12 were included in the systematic review. Of these, five of these 12 articles used quantitative methods, 3 used mixed methods, and 4 used exclusively qualitative methods. There was high heterogeneity among the studies, such as different samples, subjects, and evaluation methods. The studies included were carried out from 2008 to 2017 in 28 different countries, including Brazil, Canada, Vietnam, and some European countries. In most of these articles, Public Health was one of the main areas in the formal education of nutritionists, with predominant traditional teaching approach, with a biological focus and a fragmented curricular structure. The included studies were evaluated with low risk of bias. In spite of the limitations identified in the professional training of nutritionists, there was a clear emphasis on the importance of the relationship between theory and practice, teaching-research-extension approaches, and practical activities or internships in Public Health, which can promote the development of skills and competencies that can exert an impact on their professional performance.
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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 | 0.020 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".