Strategies for Collaboration in the Interdisciplinary Field of Emerging Zoonotic Diseases
Bibliographic record
Abstract
The integration of the veterinary, medical and environmental sciences necessary to predict, prevent or respond to emerging zoonotic diseases requires effective collaboration and exchange of knowledge across these disciplines. There has been no research into how to connect and integrate these professions in the pursuit of a common task. We conducted a literature search looking at the experiences and wisdom resulting from collaborations built in health partnerships, health research knowledge transfer and exchange, business knowledge management and systems design engineering to identify key attributes of successful interdisciplinary (ID) collaboration. This was followed by a workshop with 16 experts experienced in ID collaboration including physicians, veterinarians and biologists from private practice, academia and government agencies. The workshop participants shared their perspectives on the facilitators and barriers to ID collaboration. Our results found that the elements that can support or impede ID collaboration can be categorized as follows: the characteristics of the people, the degree to which the task is a shared goal, the policies, practices and resources of the workplace, how information technology is used and the evaluation of the results. Above all, personal relationships built on trust and respect are needed to best assemble the disciplinary strength of the professions. The challenge of meeting collaborators outside the boundaries of one's discipline or jurisdiction may be met by an independent third party, an ID knowledge broker. The broker would know where the knowledge could be found, would facilitate introductions and would help to build effective ID teams.
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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 | 0.084 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.005 | 0.047 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".