Bridging Research, Education and Practice across disciplines: Need for Nutrition Education/Innovation Programme (NNEdPro)
Bibliographic record
Abstract
Objective To build and evaluate a multidisciplinary network to translate and integrate nutrition knowledge into clinical practice and policy for health professionals in clinical, research, industry and policy settings. Background Many illnesses are preventable by early recognition and appropriate management in primary care settings. However, due to lack of training and difficulty accessing reliable nutritional evidence, health professionals find it difficult to incorporate nutrition effectively into practice. The Need for Nutrition Education/Innovation Programme (NNEdPro) developed a multidisciplinary network of health professionals to: (1) identify knowledge gaps in nutrition medical practice through primary research studies, (2) develop and deliver education and training interventions through a training academy, (3) evaluate and assess delivery methods and healthcare impact by influencing health care policy. Model of Change NNEdPro uses the knowledge‐to‐action cycle to translate nutrition evidence across the following domains: researchers, policymakers, practitioners, patients and the public. In particular, NNEdPro 1) incorporated clinical and public health nutrition into University of Cambridge's medical curriculum; 2) organised the First Annual International Summit on Medical Education Research to foster knowledge and innovation exchange; 3) established global network hubs in India, Australia/New Zealand, and Canada to support local professionals on research methodology and 4) delivered an accredited Summer School in Applied Human Nutrition to train allied‐health professionals on integrating nutrition in practice. Evaluation NNEdPro's success of its multidisciplinary model of change is illustrated through over 100 peer‐reviewed quality publications. NNEdPro introduced and edited the Special Issue on Public Health by the Royal Society of Public Health where current work on medical nutrition education and research has been displayed. NNEdPro also won the Complete Nutrition National Award for Outstanding Achievement in 2015 and designation as an Education Team of the Year by the BMJ Awards in 2016. Conclusions and Implications NNEdPro continues to develop self‐sustaining knowledge, skills and capacity in nutrition and health through: 1) A Global Training Academy delivering nutrition education to impact knowledge, attitudes and practices; 2) Consultancy services and action‐orientated research to design and conduct education and implementation programmes; 3) the NNEdPro Cambridge Foundation to facilitate public understanding of nutrition; 4) the Consortium of Research Laboratories to combine Non Communicable Disease Prevention (including Cardiovascular aspects of Nutrition) and Dietary Bioactives research capabilities. Using these four sections of specialist capability, knowledge and skills NNEdPro employs the education of health professionals as a sustainable intervention tool to enable primary research studies, generation of new evidence and translation into policy and practice. More information NNEdPro's model of change can be found at nnedpro.org.uk Support or Funding Information NNEdPro's strategic partners are: Cambridge University Health Partners, Wolfson College Cambridge, British Dietetics Association, Ulster University, Society for Nutrition Education and Behaviour, and GODAN (Global Open Data for Agriculture and Nutrition)
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 | 0.144 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".