Best Practices for Promoting Equity, Diversity, and Inclusion in Infectious Disease Modelling
Bibliographic record
Abstract
Infectious disease modelling plays a critical role in understanding and addressing global health challenges. However, the field faces persistent barriers related to Equity, Diversity, and Inclusion (EDI), including language and cultural biases, underrepresentation of certain groups, and systemic inequities in access to resources and opportunities. This paper identifies key challenges and proposes actionable best practices to foster inclusivity, collaboration, and innovation in the field. Our recommendations cover diverse contexts, including conferences, team dynamics, and virtual collaborations, and emphasize practical steps such as diversifying organizing committees, accommodating participant needs, and integrating mentorship programs. While recognizing that EDI initiatives must be tailored to specific cultural and institutional settings, we highlight the importance of measurable progress through continuous reflection, assessment, and adaptation. By embracing EDI principles, infectious disease modelling can better harness the strengths of a diverse global community, leading to more equitable science, more representative models, and improved health outcomes worldwide.
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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 | 0.247 | 0.307 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.011 | 0.033 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".