Marshalling Science for Global Health: A Bilateral Workshop
Bibliographic record
Abstract
Huge health disparities exist across regional and national boundaries; yet we also live in an interconnected world where pathogens and outbreaks that are more common elsewhere or that begin on distance shores affect our own patients and populations. Recognizing outbreaks and improving the prevention and treatment of infectious diseases are essential for improving health globally, and there has been a tremendous increase over the last decade in interest in and resources for global health. Despite this progress, many advances in healthcare still do not get implemented in low- and middle-income countries in a timely fashion, and diseases that cause substantial burdens of illness in these regions are ignored by the larger research community. Because of this, the communities are highest risk for outbreaks usually are least capable of recognizing and containing them. To address these issues, we propose to hold a workshop to develop an integrated global health surveillance, research and education network that links Canadian and California investigators with colleagues in low- and middle-income countries. New information technologies mean that poor infrastructures and remote locations may no longer be barriers to building effective surveillance and educational programs, and we have the opportunity to address global health challenges in ways not previously possible. Marshalling experts from academia, industry and public health, we would map out the available technologies needed to build a global health network capable of assisting in the formation of multi-disciplinary teams to address global health challenges and facilitating the introduction of health advances in low- and middle-income settings. Other long-term goals include identifying tools needed to improve country-wide collection of health data in low- and middle-resource settings, establishing essentially real-time surveillance of emerging public health risks from primary health data and establishing multi-disciplinary global health training programs. Canada and California have large foreign-born populations, annually receive millions of travelers from around the world and have underserved communities living in remote settings. The workshop is an important step towards building our joint capacity to better serve and protect our populations, to tackle crucial health issues for individuals most burdened by diseases, and to build world-class global health training programs. This proposal also builds on important initiatives already underway in both countries. The applicants are senior investigators with extensive experience working internationally and expertise in basic, clinical and social sciences as well as informational technology, and are well-qualified to undertake this exciting, ambitious proposal to improve health locally and globally.
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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 | 0.043 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".