Spread and Scale of the Integrated Nutrition Pathway for Acute Care Across Canada: Protocol for the Advancing Malnutrition Care Program
Notice bibliographique
Résumé
BACKGROUND: A high proportion of patients admitted to hospital are at nutritional risk or have malnutrition. However, this risk is often not identified at admission, which may result in longer hospital stays and increased likelihood of death. The Integrated Nutrition Pathway for Acute Care (INPAC) was developed to provide clinicians with a standardized approach to prevent, detect, and treat malnutrition in hospital. OBJECTIVE: The purpose of this study was to determine if the Advancing Malnutrition Care (AMC) program can be used to spread and scale-up improvements to nutrition care in Canadian hospitals. METHODS: A prospective, longitudinal, mixed methods design is proposed to evaluate the spread and scale of INPAC best practices across Canadian hospitals using a mentor-champion model. Purposive and snowball sampling are used to recruit mentors and hospital champions to participate in the AMC program. Mentors are persons with experience improving nutrition care in a clinical setting and champions are health care providers with a commitment to implementing best care practices. Mentors and champions are trained digitally on their roles and activities. Mentors meet with champions in their area monthly to support them with making practice change. Champions created a site implementation team to target practice change in a specific area related to malnutrition care and use AMC program-specific tools and resources to implement improvements and collect site information through quarterly audits of patient charts to track implementation of nutrition care best practices. An online community of practice is held every 3-4 months to provide further implementation resources and foster connection between mentors and champions at a national level. A prospective evaluation will be conducted to assess the impact of the program and explore how it can be sustainably spread and scaled across Canada. Semistructured interviews will be used to gain a deeper understanding of mentor and champion experiences in the program. The capabilities, opportunities, and motivations of behavior model will be used to evaluate behavior change and the Kirkpatrick 4-level framework will facilitate assessment of barriers to change. Aggregated chart audits will assess the impact of implemented care practices. Descriptive analyses will be used to describe baseline mentor and champion and hospital characteristics and mentor and champion experiences; Friedman test will describe these changes over time. Directed content analysis will guide interpretation of interview data. RESULTS: Data collection began in September 2022 and is anticipated to end in June 2025, at which time data analysis will begin. CONCLUSIONS: Evaluation of the AMC program will strengthen decision-making, future programming, and will inform program changes that reflect implementation of best practices in nutrition care while supporting regional mentors and hospital champions. This work will address the sustainability of AMC and the critical challenges related to hospital-based malnutrition, ultimately improving nutrition care for patients across Canada. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/62764.
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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 | 0,072 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,014 | 0,003 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».