Self-Management Characterization for Families of Children With Medical Complexity and Their Social Networks: Protocol for a Qualitative Assessment
Notice bibliographique
Résumé
BACKGROUND: Children with medical complexity (CMC) present rewarding but complex challenges for the health care system. Transforming high-quality care practices for this population requires multiple stakeholders and development of innovative models of care. Importantly, care coordination requires significant self-management by families in home- and community-based settings. Self-management often requires that families of CMC rely on vast and diverse social networks, encompassing both online and offline social relationships with individuals and groups. The result is a support network surrounding the family to help accomplish self-management of medical tasks and care coordination. OBJECTIVE: The goal of this study is to use a theoretically driven perspective to systematically elucidate the range of self-management experiences across families of CMC embedded in diverse social networks and contextual environments. This approach will allow for characterization of the structure and process of self-management of CMC with respect to social networks, both in person and digitally. This research proposal aims to address the significant gaps in the self-management literature surrounding CMC, including the following: (1) how self-management responsibilities are distributed and negotiated among the social network and (2) how individual-, family-, and system-level factors influence self-management approaches for CMC from a theoretically driven perspective. METHODS: This study will encompass a qualitative descriptive approach to understand self-management practices among CMC and their social networks. Data collection and analysis will be guided by a theoretical and methodological framework, which synthesizes perspectives from nursing, human factors engineering, public health, and family counseling. Data collection will consist of semistructured interviews with children, parents, and social network members, inclusive of individuals such as friends, neighbors, and community members, as well as online communities and individuals. Data analysis will consist of a combination of inductive and deductive methods of qualitative content analysis, which will be analyzed at both individual and multiadic levels, where interview data from two or more individuals, focused on the same experience, will be comparatively analyzed. RESULTS: This study will take approximately 18 months to complete. Our long-term goals are to translate the qualitative analysis into (1) health IT design guidance for innovative approaches to self-management and (2) direct policy guidance for families of CMC enrolled in Medicaid and private insurance. CONCLUSIONS: Multiple innovative components of this study will enable us to gain a comprehensive and nuanced understanding of the lived experience of self-management of CMC. In particular, by synthesizing and applying theoretical and methodological approaches from multiple disciplines, we plan to create novel informatics and policy solutions to support their care within home and community settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/14810.
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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 | 0,086 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,005 |
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 ».