Leadership, governance and partnerships are essential One Health competencies
Bibliographic record
Abstract
One Health is held as an approach to solve health problems in this era of complexity and globalization, but inadequate attention has been paid to the competencies required to build successful teams and programs. Most of the discussion on developing One Health teams focuses on creating cross-disciplinary awareness and technical skills. There is, however, evidence that collaborative, multi-disciplinary teams need skills, processes and institutions that enable policy and operations to be co-managed and co-delivered across jurisdictions. We propose that competencies in leadership and human resources; governance and infrastructure; and partnership and stakeholder engagement are essential, but often overlooked One Health attributes. Competencies in these staple attributes of leadership and management need to be more prominent in training and One Health capacity development. Although One Health has been in existence for over a decade, there has been no systematic evaluation of the essential attributes of successful and sustainable One Health programs. As such, much of this paper borrows from experience in other sectors dealing with complex, cross and inter-sectoral problems. Our objective is to advocate for increased investment in One Health leadership, governance and partnership skills to balance the focus on creating cross-disciplinary awareness and technical proficiency in order to maintain One Health as a viable approach to health issues at the human-animal-environment interface.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".