P02-189 - Sharing the Wealth: a Collaborative Model of Mental Health Services
Bibliographic record
Abstract
Current economic realities and funding challenges necessitate organizations to engage in creative solutions that meet ongoing and increasingly complex needs of the clients being served. The creation of the Toronto Urban Health Alliance (TUHA) is a collaborative partnership between six community health centres (CHC) and a hospital-based mental health program to maximize limited resources. Objectives 1. Understand the concept and benefits of a collaborative care model through a case based presentation 2. Become familiar with best practices for the successful implementation and ongoing service delivery of collaborative mental health care Aims TUHA's aim is to improve access to mental health services and strengthen each Community Health Centres’ capacity to meet the mental health needs of its clients through a “shared care model”. Methods This case based presentation will illustrate the benefits and promising practices of implementing such a model Results A shared care model is generally utilized within a particular organization to foster collaborative work among multidisciplinary teams. The unique feature of TUHA is the partnership between six different community health centres and mental health specialists using a common set of principles and objectives to sustain and enhance the quality of service being offered in the community. Conclusions Reflections on a multi-site partnership in the delivery of a collaborative model within a large urban centre creates a platform to determine the possibility of replicating a similar model in other communities.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".