Using Collective Impact for intersectoral action in rural Northern Ontario: Two case studies
Bibliographic record
Abstract
SETTING: Timiskaming District in Northern Ontario has a population of 32,394 people across 24 municipalities, two unincorporated areas, and four First Nations. During the time of these case studies, public health services were provided by the Timiskaming Health Unit, one of 34 local public health agencies in Ontario. INTERVENTION: To address local public health priorities in this rural region, the Timiskaming Health Unit implemented the Collective Impact framework, establishing governance structures for two initiatives: the Timiskaming Community Safety and Wellbeing Plan (CSWB) and Timiskaming Drug and Alcohol Strategy (TDAS). Acting as the backbone organization, the Health Unit facilitated a common agenda, shared progress measures, and coordinated mutually reinforcing activities. OUTCOMES: The 2023 CSWB Plan, co-funded by all 24 municipalities, established a steering committee and three working groups to address safety and well-being goals. TDAS, launched in 2022, engages over 20 organizations and community members through a steering committee and four working groups. Deliverables include public events, navigational resources, social marketing campaigns, capacity building, new health infrastructure, improved collaboration, and advocacy for healthy public policy. IMPLICATIONS: These initiatives demonstrate how local public health units can use the Collective Impact framework to address complex rural public health challenges. By integrating a continuous learning approach, implementation can integrate knowledge to foster collaboration that leads to community engagement and policy change. However, sustainable funding is critical for supporting collaborative governance and mitigating challenges like limited rural data availability.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".