Partnering with People with Lived/Living Experience of Mental Health and substance Use in Participatory Evaluation of an integrated virtual navigation support service for people experiencing substance use challenges.
Bibliographic record
Abstract
Introduction/Background: This presentation shares how the Mid-West Toronto-Ontario Health Team (MWT-OHT) in Toronto, Canada involved patients and partners in the design, implementation, and evaluation of the “In Your Corner Program” to support people with substance use challenges. The experience of embedding the voices of people with living/lived experience in co-designing and evaluating the program will be shared. Who is it for? The “In Your Corner” program represents the actualization of key recommendations from people with lived/living experience with substance use challenges in MWT-OHT that identified a need for service navigation supports that helps to close transition gaps. The ‘In Your Corner’ program necessitated the MWT-OHT to take a collaborative, team, and consensus-based approach to coordinating services across the region, re-designing care pathways that better meet the needs of a dynamic and diverse population within the constraints of existing resources and structures. Who did you involve & engage with? The MWT-OHT brings together partners across health and social sectors in the mid-west downtown region of Toronto Canada to work together for better, more integrated care. In 2019, MWT-OHT completed co-design work with people with living/lived experiences of substance use and mental health focused on improving the care experience and outcomes for people with challenges related to substance use. The In Your Corner program was implemented in 2021 through a 5-month implementation funding and extended for an additional 4 months through in-kind support from implementation partners. During this time, a participatory evaluation approach was employed with partners as well as clients and providers of the program to inform next steps, scale, and sustainability. What results did you get? What impact did you have? Initial implementation findings of the program demonstrated that the model of care capitalizes on existing programs and was able to create a more robust scaffolding for integrated care for the clients served by the program. The opportunity to embed Peer support workers into the model was highlighted along with the need for follow-up supports over a longer period. The program was a first step toward working together with people with living and lived experiences from design to evaluation from a partnered approach that is hoped to be embedded in how the MWT-OHT approaches all initiatives. What is the learning for the international audience? The learnings from this program demonstrates the processes and supports needed to meaningfully embed the voices of patients and providers from design phase through implementation, evaluation, and scale at various points in the journey from a systems perspective. Considerations for scale are important, as well as minimizing and preventing tokenism and supporting true partnering. What are the next steps? Next steps identified from the co-design evaluation sessions with clients and providers of the In Your Corner Program have informed how to leverage, change, and scale the program to reach more people in the region with substance use challenges. More importantly, this process has become a blueprint for the MWT-OHT in how to embed the voices of patients, families, and caregivers in the designing and evaluating initiatives.
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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 | 0.048 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".