Toward One Health integrated companion animal health surveillance: barriers and solutions according to expert opinion
Bibliographic record
Abstract
Objective: To explore key stakeholders' vision for a One Health integrated companion animal health surveillance system in Ontario through a series of individual semistructured interviews with members of an expert advisory group. Methods: We conducted 9 semistructured interviews with members of the expert advisory group. Interviews were conducted online via Zoom and lasted between 20 and 60 minutes. Interviews were analyzed thematically with the use of a hybrid inductive/deductive approach to coding. Results: 8 interconnected themes were identified, describing key considerations for the future design of a companion animal health surveillance system. These themes included reluctance to participate in data sharing, complexities of extracting and processing data, securing funding, consolidated vision, value, targeted outputs, potential of misrepresentation of data, and strong governance. Conclusions: Factors relating to the design of an integrated companion animal health surveillance system are highly interlinked and often driven by value. Clinical Relevance: The results of this study will be used to inform development of a companion animal health surveillance system that incorporates One Health principles through the integration of data relating environmental and public health.
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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 | 0.071 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".