Evaluating the Effectiveness of a Proactive Community Outreach and Support Team
Bibliographic record
Abstract
To better support individuals with mental health related challenges that frequently come into contact with the police, four partner organizations in London, Ontario co-developed the Community Outreach and Support Team (COAST).This program was designed to be health care led and police supported and involves a police officer and a mental health professional responding to calls together.We conducted three studies aimed at evaluating the COAST.The first study is an implementation analysis that examines whether the COAST program is operating in the way that was intended.Based on a range of data sources, we found that while many aspects of the program do align with the original implementation plan, there are also numerous discrepancies, typically due to the fact that the program was adapting operations to emerging issues (e.g., COVID-19).In the second study, we conducted pre-and post-implementation surveys with staff members working within the partner organizations, as well as satisfaction surveys with clients.The results collectively suggest that while the COAST is perceived as a valuable resource that is perceived as beneficial for clients and staff, it may not be having a broader impact on staff knowledge, attitudes, or workload due to its limited size, scope, and availability, as well as communication challenges.The final study examined the impact of the COAST from multiple perspectives based on in-depth interviews with those who had direct contact with the team.A grounded theory was developed to explain how the COAST takes individuals from suffering to enhanced connection to support.The theory also outlines the supportive conditions that facilitate this process and the key barriers that detract from it.The current dissertation contributes to the growing body of literature that examines the EVALUATING A COAST iii impact of co-response teams.While this research generally supports the value of COAST, it also highlighted specific challenges with the program and identifies areas requiring improvement.The findings are discussed in terms of their implications for the London COAST, as well as for other communities that are considering implementing similar initiatives or modifying existing ones to enhance outcomes for clients.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".