"Making Canada Whole”: Multi-Jurisdictional Collaboration as a Strategy to Advance Supply Chain Resilience for Canadian Health Systems
Bibliographic record
Abstract
This paper describes a framework that engages diverse leaders and decision-makers across Canada's federal, provincial and territorial jurisdictions to build collaboration that overcomes the silos and competition among jurisdictions during healthcare supply disruptions. The collaboration model proposes to address the challenge of fragmented and competitive approaches among Canadian jurisdictions to source and manage supply shortages, which increases the risk of harm for both patients and the healthcare workforce. Empirical evidence of outcomes and effectiveness of collaborative engagement across jurisdictions is presented to demonstrate the potential for a "Whole Canada" approach to coordinating management of supply disruptions and strategies that mitigating the risk of supply disruptions for patients and health system capacity to deliver care. Simulations were used to pilot the framework, focusing on supply management strategies that reach across Canadian jurisdictions to mitigate risks of supply shortages to ensure that all Canadians have access to safe and sustainable healthcare services.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".