1 Empowering global nutrition with digital technology – a health systems perspective
Notice bibliographique
Résumé
Advances in digital technologies impact several aspects of nutrition and healthcare science and practice. During the COVID-19 pandemic, the NNEdPro Nutrition and COVID-19 task force, supported by the BMJ Nutrition Prevention and Health journal, produced and curated evidence-based digital repositories of nutrition-related resources and educational nutrition-related materials for healthcare professionals, policymakers and the public, tailored to different geographical regions. International research collaborations increasingly use virtual platforms to link and analyse multiple sources of data from across sectors (relating to food, nutrition, and health) with potential to gain important insights into health impact and risk prediction. National and international nutrition education initiatives based on virtual networks, including the CAN DReaM (Creating Alliances Nationally to Address Disease-Related Malnutrition) project in Canada, and the Education and Research in Medical Nutrition Network (ERIMNN) in the UK, have the potential to make nutrition education more accessible across wide geographical regions. The rise of digital social media platforms allows for rapid dissemination of information at an unprecedented scale. Whilst this has been used to have a positive impact, it also carries a risk of harm through targeted misinformation and exploitative practice. For example, the recent WHO report into the digital marketing of breast milk substitute products revealed the predatory tactics that target vulnerable women and exploit parental health anxieties to promote a multi-billion dollar industry. On this topic, discussion in the Middle East and Pan-Africa regional networks satellite event of the Summit highlighted the need for health professionals to employ ‘traffic control on the digital information highway’. Perhaps one of the more tangible examples of digital technology empowering healthcare practice is the proliferation of digital smart phone apps, particularly as tools in the management of chronic health conditions. Diet and lifestyle management support apps have entered the chronic disease management space. Some that utilise artificial intelligence are in development, and in some cases in clinical trials, and clinical practice. One such app designed by Diabetes Digital Media has been integrated into some NHS weight management services in the UK. These technologies aim to better understand behaviour and lifestyle change, improve patient engagement and the sustainability of lifestyle changes, and allow granular data collection and remote monitoring of outcome variables. In developed countries, digital data platforms have been used to explore the intersection at which social determinants of health meet nutrition-related genomics and health outcomes. The challenge of severe health inequities relates closely to societal frictions and conflicts, economic market forces, and the health of education and food systems. To have the greatest impact on nutrition globally, digital technologies must account for and address health inequities that underlie the risk of malnutrition and poor health of millions of people. Furthermore, health systems do not operate in isolation. Empowering individuals and populations to live healthy lives requires a collective buy-in from the education sector. Therefore, at the Summit, several digitally-assisted educational schemes based in primary schools, community settings, medical schools, and healthcare systems in various global regions were showcased. Measuring and validating the safety and efficacy of novel digital technologies for better nutrition and health is essential for ensuring positive impacts.
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 | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,010 |
| Communication savante | 0,018 | 0,015 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,010 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,003 |
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 ».