Bridging Research, Education and Practice across disciplines: Need for Nutrition Education/Innovation Programme (NNEdPro)
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,144 | 0,123 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,011 | 0,009 |
| Science ouverte | 0,007 | 0,032 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».