From intention to action – cultivating future-ready One Health agents of change
Bibliographic record
Abstract
Abstract We used a narrative literature review to identify attributes of One Health practitioners who can close the gap between intention and action to protect and promote health in this era of polycrises. The intention in this essay was to instigate discourse that challenges the current state of One Health teaching and practice, thus helping us reflect on how to future-ready One Health. One Health researchers and practitioners must become agents of change who accelerate and amplify innovations that promote One Health as a settings-based approach to advance interspecies and intergenerational health equity. This essay outlines how future readiness and disruption are intertwined and proposes that One Health training needs to cultivate curiosity, agility and convergence thinking to create future-ready researchers and practitioners. Institutional systems that can support future-ready One Health agents of change will need to be attentive to mechanisms that close the knowing-to-doing gap and promote crossing barriers. Game changing One Health requires greater investment in cross-cutting capacities and ideas that will make it easier to see what is working and for whom. At the heart of this issue is the need to mainstream concepts of fairness and redistribution of the health resources between people, animals, and settings.
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".