Navigators Collaborative: Building capacity within a community by understanding resources and roles better
Notice bibliographique
Résumé
Background: Navigators are playing an increasingly important role in supporting community members find help. Understanding the different services available, who provides them, and the criteria needed to access them has become a challenge across sectors. Community members (I.e. patients and caregivers) and providers are finding it challenging to navigate health and social services. A current state analysis was completed of agencies in the Middlesex London (Ontario, Canada) area and participants agreed a Navigators Collaborative would assist in mitigating this challenge. A diverse planning team was established with representation from health and social sectors, rural and urban environments, and marginalized and racialized communities, to create the Middlesex London Navigators Collaborative (MLNC). Objectives: The MLNC purpose, as outlined in the terms of reference, is to facilitate knowledge exchange, promote warm transfers by establishing pathways, and establish and build relationships. Inviting service providers from across sectors to this platform increases collaboration and communication between them and hence increases opportunities for integrated care. Understanding the purpose of each others’ organizations and the programs provided allows smoother communication, facilitates seamless transitions, and decreases inappropriate referrals. This improves awareness of and access to services available in the Middlesex London area and improves the overall patients’/clients’, caregivers’/care partners’, and healthcare providers’ experiences. The collaborative began meeting October 2022 and meets on a bi-monthly basis with an average of 30 attendees. Highlights: The MLNC has been well-received . A survey-based evaluation was completed in March 2023 when MLNC members were asked if the collaborative was meeting its purpose as indicated above. After only three meetings, 75% agreed, with over half of the respondents already having connected a patient/client with a service provider met through the collaborative. A centralized database has been established through Microsoft Teams to share program information and members’ contact information for ease of communication. Neighboring health teams have also reached out for copies of the terms of reference and other information to model their collaborative efforts after this one. The “boots on the ground” experience of the collaborative members has also provided valuable insight in co-design for other local health system projects. Conclusion: As the collaborative reaches its first year mark, we are now looking at how we can advocate for change within our organizations. Smaller working groups will be created to look at different initiatives that were developed by the collaborative members. Initiatives will include referral processes, communications, and how to support each other with the increasing capacity challenges. The collaborative will continue to take its lead from the members as to how to improve patients’/clients’, caregivers’/care partners’, and healthcare providers’ experiences throughout the health journey. Conference Themes: 1.Partnerships, collaborations, and new alliances 2.From evidence to policy and from policy to practice 3.Supporting the health and care workforce 4.Delivering integrated care in the community 5.The role of general practice and primary care in integration 6.Meaningful use of digital solutions and shared data for information and care management
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 | 0,014 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,017 | 0,012 |
| Communication savante | 0,012 | 0,020 |
| Science ouverte | 0,004 | 0,048 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 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 ».