INNOVATX Global Health Case Competition 2023 – Presented by McMaster Friends of MSF
Bibliographic record
Abstract
McMaster Friends of MSF (FoMSF) is a student-led club at McMaster University that supports Medecins Sans Frontieres (MSF) Canada, a humanitarian relief-based organization that helps countries across the world. McMaster FoMSF organized the INNOVATX Global Health Case Competition to provide undergraduate students with the chance to problem-solve, enrich their skills, and above all, gain valuable exposure to global health. This year’s competition focused on the health consequences of the Syrian civil war. In particular, participants aimed to tackle a specific health-related issue of their choosing, which is currently affecting approximately 1.5 million Syrian refugees living in Lebanon, especially those residing in one of the country’s official refugee camps. After a round of written submissions and another round of live presentations, the briefing notes from the four winning teams have been published in this conference book. To learn more about McMaster FoMSF or the INNOVATX Global Health Case Competition, please visit our Instagram (@mac_fomsf) or Facebook (McMaster Friends of MSF) pages. Disclaimer: The views expressed throughout this case competition and publication are solely those of the McMaster FoMSF team and INNOVATX participants and do not reflect those of MSF Canada, McMaster University, or any other organization.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.144 | 0.024 |
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".