Supporting community overdose response planning in Ontario, Canada: Findings from a situational assessment
Bibliographic record
Abstract
Abstract Background Many communities across North America are coming together to develop comprehensive plans to address and respond to the escalating overdose crisis, largely driven by an increasingly toxic unregulated drug supply. As there is a need to build capacity for successful implementation, the objective of our mixed methods study was to identify the current planning and implementation practices, needs, and priority areas of support for community overdose response plans in Ontario, Canada. Methods We used a situational assessment methodology to collect data on current planning and implementation practices, needs, and challenges related to community overdose response plans in Ontario, consisting of three components. Between November 2019 to February 2020, we conducted ten semi-structured key informant interviews, three focus groups with 25 participants, and administered an online survey (N = 66). Purposeful sampling was used to identify professionals involved in coordinating, supporting, or partnering on community overdose response plans in jurisdictions with relevant information for Ontario including other Canadian provinces and American states. Key informants included evaluators, representatives involved in centralised supports, as well as coordinators and partners on community overdose response plans. Focus group participants were coordinators or leads of community overdose response plans in Ontario. Results Sixty-six professionals participated in the study. The current planning and implementation practices of community overdose response plans varied in Ontario. Our analysis generated four overarching areas for needs and support for the planning and implementation of community overdose response plans: 1) data and information; 2) evidence and practice; 3) implementation/operational factors; and 4) partnership, engagement, and collaboration. Addressing stigma and equity within planning and implementation of community overdose response plans was a cross-cutting theme that included meaningful engagement of people with living and lived expertise and meeting the service needs of different populations and communities. Conclusions Through exploring the needs and related supports for community overdose response plans in Ontario, we have identified key priority areas for building local capacity building to address overdose-related harms. Ongoing development and refinement, community partnership, and evaluation of our project will highlight the influence of our supports to advance the capacity, motivation, and opportunities of community overdose response plans.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".