Developing the Tools to Manage Complex Crises
Bibliographic record
Abstract
With an increase in globalization and the rise of new and reemerging diseases, there is potential for widespread disease outbreaks and dissemination. Evidence shows individuals with an established appreciation for, and understanding of, an interdisciplinary framework for problem solving have an advantage in dealing with major global crises. The Integrated Training Program in Infectious Disease, Food Safety and Public Policy (ITraP) was recently developed at the University of Saskatchewan, Canada, to build these interdisciplinary skills in young professionals. This article presents the benefits and advantages of this type of training, by providing real-world examples of how knowledge and skills emphasized in ITraP teachings provide methods to assist in controlling epidemic situations. Moreover, to further the conversation about these training programs and to aid groups who are considering developing similar programs, this article discusses lessons learned from the first few years of ITraP’s inception, including the major barriers to success. We found that although interdisciplinary training programs are becoming increasingly necessary to deal with problems in our complex world, there are still a multitude of obstacles to be considered prior to the development and implementation of such a multifaceted program. Therefore, it is important that as these types of training programs begin to grow and evolve, researchers begin a dialogue regarding what types of teaching methods to employ, what interdisciplinary theories to use, and whether there is any evidence of success and sustainability.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".