Pedi-R-MAPP: The development of a nutritional awareness tool for use in remote paediatric consultations using a modified Delphi consensus
Notice bibliographique
Résumé
BACKGROUND & AIMS: The Remote Malnutrition Application (R-MAPP) was developed during the COVID-19 pandemic to provide support for health care professionals (HCPs) working in the community to complete remote nutritional assessments, and provide practical guidance for nutritional care. The aim of this study was to modify the R-MAPP into a version suitable for children, Pediatric Remote Malnutrition Application (Pedi-R-MAPP), and provide a structured approach to completing a nutrition focused assessment as part of a technology enabled care service (TECS) consultation. METHODS: A ten-step process was completed: 1) permission to modify adult R-MAPP, 2) literature search to inform the Pedi-R-MAPP content, 3) Pedi-R-MAPP draft, 4) international survey of HCP practice using TECS, 5) nutrition experts invited to participate in a modified Delphi process, 6) first stakeholder meeting to agree purpose/draft of the tool, 7) round-one online survey, 8) statements with consensus removed from survey, 9) round-two online survey for statements with no consensus and 10) second stakeholder meeting with finalisation of the Pedi-R-MAPP nutrition awareness tool. RESULTS: The international survey completed by 463 HCPs, 55% paediatricians, 38% dietitians, 7% nurses/others. When HCPs were asked to look back over the last 12 months, dietitians (n = 110) reported that 5.7 ± 10.6 out of every 10 appointments were completed in person; compared to paediatricians (n = 182) who reported 7.5 ± 7.0 out of every 10 appointments to be in person (p < 0.0001), with the remainder completed as TECS consultations. Overall, 74 articles were identified and used to develop the Pedi-R-MAPP which included colour-coded advice using a traffic light system; green, amber, red and purple. Eighteen participants agreed to participate in the Delphi consensus and completed both rounds of the modified Delphi survey. Agreement was reached at the first meeting on the purpose and draft sections of the proposed tool. In round-one of the online survey, 86% (n = 89/104) of statements reached consensus, whereas in round-two 12.5% (n = 13/104) of statements reached no consensus. At the second expert meeting, contested statements were discussed until agreement was reached and the Pedi-R-MAPP could be finalised. CONCLUSION: The Pedi-R-MAPP nutrition awareness tool was developed using a modified Delphi consensus. This tool aims to support the technological transformation fast-tracked by the COVID-19 pandemic by providing a structured approach to completing a remote nutrition focused assessment, as well as identifying the frequency of follow up along with those children who may require in-person assessment.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».