Climate change professionals’ perspectives on the competencies for One Health graduates
Bibliographic record
Abstract
Abstract There is a pressing need for novel approaches to help address climate change and for a workforce that is equipped with a combination of new and different types of knowledges. The One Health (OH) core competencies perhaps offer the new knowledges, skills and attitudes that will be needed in a future generation of practitioners that does not shy away from complexity. The objective of this research was to identify overlapping and transferable OH-climate change competencies that are needed of professionals working to address climate change. Using focus groups and qualitative content analysis, 23 professionals from across Canada whose employment positions had a key focus on climate change were brought together across five sessions. Participants agreed that the OH competencies were applicable to their employment roles and responsibilities, but they identified four key missing areas that are important for graduates: evaluative and reflective practice, personal resilience, turning knowledge into action and having an openness to other knowledges (particularly Indigenous and non-Western viewpoints). This work also provided a first iteration of a process that should be continually used to bridge the gap between theory and practice, as employer needs are a key consideration during the development of educational programs.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".