Notice bibliographique
Résumé
We thank Dr. Pippitt, Dr. Junkins, and Ms. Baggaley for their interest in our article on Project ECHO. We are pleased to see discussion being generated from this manuscript. The authors accurately note that our fidelity assessment was limited to quantitative metrics, which limits our ability to extrapolate the ECHO model to chronic disease conditions that do not have clear quantitative outcomes. However, we believe that an assessment of fidelity is important for analyzing the literature to date and to inform future research related to Project ECHO. In Ontario, we are currently using qualitative methodologies to further understand learning and evaluate outcomes for Project ECHO in symptom-based diseases, such as mental health and addictions. While we agree that further exploration of the effectiveness of “learning loops” is needed, we have found in our ECHO Ontario Mental Health program that ECHO is not only beneficial to primary care physicians (PCPs) but can also be beneficial to a broader interprofessional team. There have been previous published studies of ECHO models utilized by other health care professionals, such as pharmacists, social workers, and nurse practitioners, which can expand the learning loops and team engagement. We acknowledge the need for further research to identify attributes of primary care providers engaged in ECHO to inform primary care engagement strategies. We are currently investigating practice attributes of ECHO versus non-ECHO participants and are also using qualitative methodology to better understand the learning process and knowledge transfer mechanisms within an ECHO model focused on mental health. Furthermore, we agree that some ECHO primary care providers may not perceive a need to change practice patterns; however, it is purported that comanagement of cases and iterative reflection during ECHO sessions can be useful in highlighting opportunities for practice improvement. In Ontario, we have encountered similar challenges in the recruitment of PCPs, likely due to the reimbursement model challenges to account for PCP time. Despite these challenges, we have demonstrated high engagement and retention (93%) with other primary care providers, such as nurse practitioners in rural sites. Our review underscores the need for additional evaluation data, using both quantitative and qualitative methods, to determine ECHO’s efficacy and cost-effectiveness in additional symptom-based diseases, such as headache, chronic pain, and mental health. We hope that additional research on ECHO participation and learning will improve Project ECHO implementation efforts in a broad range of contexts and practice settings. Sanjeev Sockalingam, MD, MHPECo-chair, ECHO Ontario Mental Health, and associate professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; [email protected] Carrol Zhou, MDPsychiatry resident, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Allison Crawford, MD, MACo-chair, ECHO Ontario Mental Health, and assistant professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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 | 0,009 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,006 | 0,010 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,038 | 0,062 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,012 |
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 ».