Zoonoses and social determinants of health: A consultation of Canadian experts
Bibliographic record
Abstract
This study aimed to identify priorities for transdisciplinary research on zoonotic diseases (ZDs) using a One Health perspective. In 2017, 69 Canadian experts from various disciplines participated in a three-round Delphi prioritization exercise. Round 1 started with three ZD-related research axes: the convergence between zoonoses and chronic diseases, social determinants of zoonoses, and health system effectiveness in zoonosis prevention and control. Each included a list of potential research questions, and respondents were invited to propose additional topics for each axis. The next two rounds reduced the number of topics. Three priority research questions were ultimately selected: 1) What is the evidence that zoonoses contribute to the burden of chronic disease? 2) What do we know about the populations most vulnerable to zoonoses? 3) What do we know about the effectiveness of zoonosis prevention and control strategies? The results provide a unique view of important research needs in three ZD-related areas.
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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".