The Hub model: It’s time for an independent summative evaluation
Bibliographic record
Abstract
Over the past decade, governments and the non-profit, private, academic, and philanthropic sectors have begun thinking differently about how human and social services are organized and delivered. Across Canada, a range of integrated health and social care practices are being developed, adapted, and implemented to meet local needs. The Hub (or Situation Table as it is more commonly known in Ontario) model is one such approach. The Hub model is a multi-sector, collaborative, risk-driven intervention that mobilizes multi-sectoral human services for the purpose of rapid risk mitigation focused on the immediate needs of persons experiencing acutely elevated risk of harmful safety or well-being outcomes. Over the past eight years, the model has been adopted in over 115 communities across Canada.While the model has benefited from developmental and formative evaluations, it is now timely to undertake a systematic multi-site evaluation of the generalizable impacts (e.g., clients, system, costs) and lessons learned about what works, in which context, and why. This body of work will serve to inform policymakers, funders, practitioners and others as to the way forward with the Hub model. The Community Safety Knowledge Alliance (CSKA) is moving forward on a plan to see such independent evaluation undertaken.
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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 | 0.582 | 0.572 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.024 | 0.036 |
| Open science | 0.016 | 0.025 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".