Élaboration et évaluation formative du Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) : applications durant la pandémie de COVID-19 et futures applications
Bibliographic record
Abstract
La pandémie de COVID-19 a mis en évidence les limites de l’infrastructure actuelle de données sur la santé publique et le besoin d’un système de gestion des données en temps réel complet, centralisé et convivial qui convienne à la fois à la surveillance des maladies et à la gestion des éclosions. Pour remédier à la situation, le Groupe des Services de santé des Forces canadiennes a élaboré le Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) sur le Web. Cet article décrit en détail l’élaboration du SSGE FAC, fournit les résultats de l’évaluation formative du SSGE FAC et présente une analyse des constats dégagés et des perspectives d’utilisation du SSGE FAC pour améliorer la capacité de surveillance des maladies et de gestion des éclosions des FAC après la pandémie de COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".