Transparency or Bust: An Ontario Health Team's Experience Openly Communicating Progress and Results in a Rapid Improvement Community Pilot Project
Bibliographic record
Abstract
This oral paper will present a community-based transformative case study from the Cambridge North Dumfries Ontario Health Team (CND OHT), focusing on the transparent and collaborative approach to collecting and disseminating measures of progress, results and impact, and the benefits of this approach in a maturing locally-driven integrated care system. The CND OHT Community Mental Health and Addictions Clinic moved from co-design idea to doors open, in just three months. Over the course of the two-month pilot, 123 clients were served via 451 appointments, including virtual follow-up visits. This resulted in 23 emergency department diversions, redirecting appropriate mental health and addictions visits from the hospital. Of the 123 clients, 50% indicated that if the clinic did not exist, they would have gone to the emergency room, and 94% of clients felt that their immediate needs were adequately addressed by the clinic. Much of the success of the initiative was attributed to transparent and collaborative processes, rapid implementation, and the crucial involvement of patients and community members. Importantly, transparency extended beyond implementation, with detailed evaluation results and user-friendly infographics accessible on the Health Team's website, effectively communicating both the challenges and benefits of the program to the public. This commitment to openness not only facilitated a thorough understanding of the project's results and impact but also served as a valuable model for broader population health management initiatives. Attendees of this presentation will gain insights into the rationale behind transparent planning and evaluation, and will hear how this transparency can build trust and engagement in integrated care planning.
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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 | 0.054 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".