Characterizing Project ECHO Autism Case Recommendations and Implementation
Notice bibliographique
Résumé
Background: The Extension for Community Health Outcomes (ECHO) model creates virtual communities to help mitigate the barriers to treating complex conditions, including autism spectrum disorder (ASD; autism). ECHO Ontario Autism was developed to build province-wide capacity to diagnose autism by building community providers' skills; and 2) to improve provider understanding and confidence around autism and available therapies and supports. ECHO Ontario Autism attempts to achieve these goals through multipoint video conferencing that connects community providers with peers and specialists who can offer education, guidance on cases, and support. This study aims to determine the types of recommendations provided through the ECHO Autism Ontario program, identify reasons some recommendations were not enacted, and summarize how recommendations have impacted the practice of clinicians who present cases within ECHO didactic sessions. Approach: To code the types of recommendations provided within the ECHO Ontario Autism cases, two researchers coded deductively with a pre-existing coding guide from a previous evaluation of this program. Two researchers used an inductive approach to developing codes to categorize reasons recommendations were rejected and the impact of ECHO Ontario Autism. A summative content analysis was used to determine the frequencies with which these categories occurred. The coding guide and categories were reviewed with the broader team at regular meetings. Results: The final analysis included a total of 32 cases presented by 8 individuals, culminating in 289 recommendations. Across the 32 cases, 74% of recommendations were implemented (n = 24). This[MP] study emphasized the importance of resources in autism care, finding that accessing community resources and resources and tools for further learning were the two most common categories of recommendations, with implementation rates over 75%. While all implementation rates were generally high, recommendations that were not enacted were most often not due to reasons relating to the child/family, including the child or family declining the recommendation, the family seeking alternative resources, and the provider feeling that the family was not ready for the recommendation. This study also summarized the impact of ECHO Ontario Autism on clinical practice. Providers indicated that ECHO Ontario Autism positively influenced their approach to care for the relevant case, impacted their practice broadly beyond their ECHO cases, increased their diagnostic capabilities, and provided interpersonal benefits both with families and colleagues. Implications: This information will help to increase the utility of the recommendations provided in the ECHO Ontario Autism program and provide broader insights into barriers and facilitators of community-based autism practice. Particularly, this research emphasizes the necessity for autism-care recommendations to be relevant and well-explained to families by physicians in order for them to be successfully implemented. Additionally, this research points to barriers to care, including financial and access barriers, that must be mitigated to increase the efficiency of autism care within the community. This research also points to the vitality of the community as a key component of the ECHO Ontario Autism program.
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 | 0,059 | 0,122 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,005 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».