From Design to Action: Participatory Approach to Capacity Building for Local Overdose Response
Bibliographic record
Abstract
Abstract Background In response to the rise in opioid-related deaths, communities across Ontario have developed opioid or overdose response plans to address issues at the local level. Public Health Ontario (PHO) leads the Community Opioid / Overdose Capacity Building (COM-CAP) project, which aims to reduce overdose-related harms at the community level by working with communities to identify, develop, and evaluate capacity building supports for local needs around overdose planning. The ‘From Design to Action’ co-design workshop used a participatory design approach to engage communities in the requirements for capacity building support. Methods A participatory approach (co-design) provided opportunity for collaborative discussion around capacity building needs at the community level. The co-design workshop included three structured collaborative activities (i.e., identifying details of priority challenges, support delivery mechanisms, and evaluation planning), and was conducted with fifty-two participants involved in opioid/overdose-related plans in Ontario. Participatory materials were informed by the results of a situational assessment (SA) data gathering process, including survey, interview, and focus group data. A voting system, including dot stickers and discussion notes, was applied to identify priority supports and delivery mechanisms. Results The workshop resulted in identifying key challenges and priority supports to consider for development and implementation. Key findings were summarized into five major priorities, including: 1) stigma & equity; 2) trust-based relationships, consensus building & on-going communication; 3) knowledge development & on-going access to information and data; 4) tailored strategies and plan adaptation to changing structures and local context; and 5) structural enablers and responsive governance. Conclusion Using a participatory approach, the workshop provided an opportunity for sharing, generating, and mobilizing the required knowledge to address research-practice gaps at the community level. The application of health design methods such as the ‘From Design to Action’ co-design workshop allows for teams to gain a deeper understanding of issues as well as enhances the foundation and application of participatory approaches in addressing complex public health issues such as the overdose crisis.
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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 | 0.117 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".