McMaster Health Forum: Towards a Canadian Response to Emerging Global Health Issues
Bibliographic record
Abstract
Health is now truly global. Health crises in one part of the world can affect the health of people everywhere. Governments around the world are increasingly recognizing the importance of acting upon global health issues as a means of protecting national health security. They have begun to invest in the necessary governmental infrastruc-ture and domestic partnerships needed to coordinate a national response to address global health issues. Norway, Switzerland and the United Kingdom, among others, have now developed national global health strategies that ar-ticulate national global health objectives and the means by which government agencies and departments can co-operate towards achieving them. Canada has not yet developed anything similar; its e!orts to address global health issues remain largely uncoordinated and reactive. Ahmad AlKhatib and Theresa Tang, McMaster Health Fo-rum Fellows (2010-11), discuss the Forum’s planned stakeholder dialogue on “health and emerging global issues” – a key first step in the development of an evidence-informed Canadian response to emerging global health issues.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.041 | 0.021 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".