41 Content Analysis of the Recommendations from Project Echo Ontario Autism
Notice bibliographique
Résumé
Abstract Background The rising prevalence of autism spectrum disorder (ASD) diagnoses has caused an increased number of community practitioners to care for this population. However, community practitioners report a lack of knowledge and confidence in treating these patients, resulting in unmet healthcare needs. The Extension of Community Healthcare Outcomes (ECHO) Autism model aims to address this through case-based and didactic learning to help guide community practitioners in providing comprehensive, best-practice care for ASD screening, diagnosis, and management of co-occurring conditions. Each ECHO session involves a case presentation followed by a list of recommendations generated by community participants and an interdisciplinary ‘hub’ team. While ECHO Autism has been shown to improve physicians' abilities to care for children with ASD in their practices, recommendations stemming from ECHO cases have yet to be characterized and may help guide future care. Objectives To quantify and characterize the common categories within ECHO Autism Ontario case recommendations. Design/Methods A content analysis of 422 recommendations from 61 ECHO cases was conducted to identify categories of recommendations and their frequencies. Three researchers independently coded recommendations from five ECHO cases, from which an original coding guide was developed. The researchers then independently coded the remaining cases and met regularly with the ECHO lead to modify and consolidate the codes and coding guide. From there, categories and sub-categories from the various codes were identified. Finally, the frequencies of each code and category were calculated. Results Fifty-seven codes were included in the final coding guide and grouped into eight broad categories. Categories included: 1) diagnosis; 2) concurrent mental and physical health conditions; 3) referrals to allied health providers and other specialists; 4) accessing community resources, such as parent and sibling support groups; 5) providing education and guidance to physicians, patients, and families; 6) management strategies such as nutrition, physical activity, and social skills; and 7) patient and family-centered care. A COVID-19 category was added, as many of the later recommendations were adapted to online service delivery. An analysis of the frequency of codes found that 1,384 total in-text codes were distributed amongst the various categories. The three highest frequencies of categories were providing general guidance and education (22%), accessing resources (16%), and referrals (15%). Conclusion This is the first time recommendations from ECHO Autism have been characterized and quantified. Our results, particularly the most common category of providing general guidance and education about ASD, show there is still important work to be done with educating clinicians and families about aspects of ASD. Furthermore, findings from this study should inform Pediatrics residency programs about real-world knowledge gaps in ASD care, and may help create more tailored ASD training programs and educational materials.
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 | 0,026 | 0,099 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».