<i>A Nutrition Odyssey:</i> Knowledge Discovery, Translation, and Outreach 2006 Ryley-Jeffs Memorial Lecture
Bibliographic record
Abstract
BACKGROUND: The 21st-century model of health research is founded on a broad base of multidisciplinary research that is expeditiously and effectively translated into evidence-based practice, education, policy, and advocacy. The key objective is to improve the health of populations. DIETITIANS' ROLES: Dietitians, whether they are working in clinical or public health nutrition or food science, have a vital role to play in this paradigm of health research. As dietitians' roles have evolved beyond the traditional ones into subspecialties including epidemiology, nutrigenomics, functional foods, nutraceuticals, toxicology, natural health products, and multidisciplinary research, the need for advanced training in subspecialty fields has become essential. OPPORTUNITIES: A dietetics background is an excellent foundation upon which to develop a research career in one of these new areas of nutrition. Opportunities for personnel awards and research funding targeted at nutrition clinician-scientists and other nutrition subspecialists have grown tremendously in recent years. CONCLUSION: The information in this paper is intended to inspire dietitians seeking advanced academic training in one of the new exciting avenues for a career in nutrition. These avenues will permit dietitians to contribute to knowledge discovery, translation, and outreach to improve the nutritional status and health of populations in Canada and globally.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".