An expert opinion process to prioritize One Health information needs during a zoonotic disease outbreak
Bibliographic record
Abstract
There is a global movement to implement a One Health approach across sectors to holistically address emerging issues that have implications for public, animal, and environmental health. The operationalization of a One Health model can support knowledge sharing and build an evidence base for designing research programs and decision-making tools to evaluate and mitigate intersectoral health challenges. In late 2021, the highly pathogenic avian influenza virus (HPAIV) H5N1 2.3.4.4b was detected in eastern Canada, and subsequently spread throughout the flyways of North America. Given the multiyear persistence of the current HPAIV in Europe and the continued detections in North America, Environment and Climate Change Canada and partners recognized the need to prioritize HPAIV-related information needs to inform future decision-making and management. In early 2023, we carried out an expert opinion exercise with partners from across One Health domains and expertise to prioritize information needs related to the conservation and management of migratory birds in Canada. The results informed on-the-ground programming for migratory bird activities in 2023 and onwards. The process illustrates how a One Health lens can be applied with a conservation focal point, using dedicated facilitation to synthesize expert opinions across groups with non-overlapping mandates.
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 | 0.151 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".