Navigators Collaborative: Building capacity within a community by understanding resources and roles better
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.048 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".