Barriers to implementing One Health training programs: Experiences of university faculty and staff
Bibliographic record
Abstract
Abstract Background: To meet the wicked challenges of today, there have been calls to disrupt the status quo of educational systems. One Health (OH) was one approach identified to be well-positioned to evoke change in the learning environment and transform the way the future society addresses health challenges. However, this disruption to the educational system cannot occur without first recognizing the context (i.e., the university structures) within which OH programs are implemented, and the challenges that context imposes on such programs. Therefore, this study aimed to present the identified barriers and opportunities for OH program development and delivery within the Canadian higher education system. Methods: Focus groups were used to bring together Canadian university faculty members and staff who worked, taught, or conducted research in OH-related areas, and who were representative of human, animal, and environmental health program areas. Reflexive thematic analysis was used to analyze the transcripts. Results: Three focus group discussions were conducted with a total of 13 participants from across Canada. Six main barriers emerged during the focus group discussions: (1) One Health is abused as a brand, (2) there is the potential for the erosion of the breadth of perspectives that were built into OH programs, (3) there is inconsistent support for OH across levels of administration, (4) the university business model impacts decision-making, (5) community collaborations are stifled within current structures, and (6) faculty members’ workload agreements hinder the ability to provide transdisciplinary teaching. Opportunities were also identified that may help overcome these barriers and included: the elements of the OH approach can be delivered irrespective of the program being called “One Health”, the breadth of perspectives involved in the program can be maintained through continuous evaluation, and existing community engagement opportunities can be leveraged to support better transdisciplinary education. Conclusions: This study highlighted the barriers that faculty members and staff involved in OH program development, implementation, and evaluation have experienced and are trying to mitigate. This work will also help inform an OH program evaluation strategy for Canada.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".