Setting priorities for research in medical nutrition education: an international approach
Bibliographic record
Abstract
OBJECTIVES: To identify the research priorities for medical nutrition education worldwide. DESIGN: A 5-step stakeholder engagement process based on methodological guidelines for identifying research priorities in health. PARTICIPANTS: 277 individuals were identified as representatives for 30 different stakeholder organisations across 86 countries. The stakeholder organisations represented the views of medical educators, medical students, doctors, patients and researchers in medical education. INTERVENTIONS: Each stakeholder representative was asked to provide up to three research questions that should be deemed as a priority for medical nutrition education. MAIN OUTCOME MEASURES: Research questions were critically appraised for answerability, sustainability, effectiveness, potential for translation and potential to impact on disease burden. A blinded scoring system was used to rank the appraised questions, with higher scores indicating higher priority (range of scores possible 36-108). RESULTS: 37 submissions were received, of which 25 were unique research questions. Submitted questions received a range of scores from 62 to 106 points. The highest scoring questions focused on (1) increasing the confidence of medical students and doctors in providing nutrition care to patients, (2) clarifying the essential nutrition skills doctors should acquire, (3) understanding the effectiveness of doctors at influencing dietary behaviours and (4) improving medical students' attitudes towards the importance of nutrition. CONCLUSIONS: These research questions can be used to ensure future projects in medical nutrition education directly align with the needs and preferences of research stakeholders. Funders should consider these priorities in their commissioning of research.
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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 | 0.632 | 0.525 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".