1 Putting research into practice: knowledge translation and implementation for action on nutrition
Bibliographic record
Abstract
The transfer of research evidence into practice has been historically slow, and requires an integration of many elements, including quality evidence, supportive physical and intellectual environments, and facilitation, as discussed at the NNEdPro Sixth International Summit on Nutrition and Health. Examples of applying clinical research into practice focused on the use of group consultations (also known as group clinics or shared medical appointments) to support behaviour change, the role of dietary micronutrients during the COVID-19 pandemic and the potential of Precision Nutrition. An emerging area from early implementation evidence includes group consultations, also known as shared medical appointments, as discussed by Dr Fallows. Group consultations have been shown to improve clinical outcomes for some patient groups (e.g., HbA1c, lipids, BMI), as well as improve self-care and health education, and patient and clinician satisfaction. These groups have been piloted throughout the UK both face-to-face and virtually, with initial findings suggesting they are feasible and acceptable to patients and clinicians. Further work is needed to assess whether these could be cost-effective when scaled-up in National Health Service UK primary care. During the COVID-19 pandemic, there has been increasing emphasis on the central role of nutrition in health, including the role of dietary micronutrients, as discussed by Dr Van Dael and Shane McAuliffe. Nutrition plays an important role in immunity, yet the nutritional status of the most vulnerable population groups is likely to deteriorate further due to the health and socio-economic impacts of the novel coronavirus. Thus, implementation of this evidence into health care practice is key. Precision Nutrition, defined as an ‘approach that uses information on individual characteristics to develop targeted nutrition advice, products or services’, offers an exciting opportunity to further individualise dietary advice for behaviour change, as discussed by Dr Kohlmeier and Dr Hernandez. Precision nutrition is underpinned by the recognition that individuals differ in many important ways due to identifiable molecular traits and can be utilised to determine personalised weight loss interventions based on genetic variants. Use of implementation science is in line with one of the six cross-cutting pillars of the Nutrition Decade: Aligned health systems for universal coverage of nutrition actions. Dr Bell, an Advanced Accredited Practising Dietitian in Australia, provided an overview of key implementation science models and frameworks. Implementation frameworks such as the Action Research Framework, the Knowledge to Action Cycle, and the Spread and Sustain Framework, are underpinned by knowledge creation, effective education, and culture change. Dr Bell then highlighted how theoretical frameworks have provided guidance for the implementation of real world, complex nutrition interventions, including the Systematised Interdisciplinary Program for Implementation and Evaluation (SIMPLE) in Australia, and the More-2-Eat program in Canada.
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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 | 0.245 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".