24 Community provider perspectives on an autism learning health network: A qualitative study
Notice bibliographique
Résumé
Abstract Background Although autism is highly prevalent, no single care centre has a sufficient number of patients to produce generalizable knowledge; this slows the pace of quality improvement research. Learning health networks (LHNs) offer a promising solution to improve the quality and efficiency of healthcare by integrating clinical and research data. The Autism Care Network (ACNet) is an LHN composed of 20 clinics across the United States and Canada dedicated to developing the most effective approach to care for children with autism. In Canada, the majority of autistic patients receive a majority of their ongoing care in the community. Therefore, to make meaningful improvements in the continuum of care for patients and their families, it is essential that the ACNet be extended to include community physicians. Objectives The primary objectives were to (1) understand the current data collection practices, learning needs, clinic capacity, areas of improvement, and overall interest of community physicians in participating in a LHN and (2) identify community physicians’ perspectives on the benefits and disadvantages of participating in a LHN and ways in which their engagement in a LHN can be supported. Design/Methods In-depth semi-structured interviews were conducted participants who were purposively sampled from community providers who had previously participated in autism-focused educational programming. The interview guide was developed by the three ACNet sites based on experience with LHNs and working with community physicians. Data were analyzed using an interpretative phenomenological analysis approach with a social constructivist paradigm. Results Analysis of 29 interviews identified 5 primary themes: (1) “Navigating Administrative Challenges,” which highlighted the lack of time, resources, and administrative capacity in the community; (2) “Improving Data Collection Practices”, which emphasized the barriers to consistently collecting information from patients and families; (3) “Increasing Provider Confidence and Competence,” which explored the challenges of navigating the everchanging landscape of community autism care; (4) “Breaking Down Silos”, which focused on fragmented care and the lack of communication between allied health resources, diagnostic hubs, and community physicians; and (5) “Systemic and Societal Barriers to Achieving Best Practices,” which explored the systemic barriers that impact autism care for physicians and families. Conclusion This study provides an understanding of the experiences of community physicians regarding the challenges of community-based autism care. LHNs have the potential to address several of the issues highlighted by community physicians in autism care. Additionally, LHNs can play an important role in improving community care in fields beyond autism.
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 | 0,016 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,008 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».