Qualitative description of One Health perception, educational opportunities, and goals of students in programs related to human, animal, and environmental health
Bibliographic record
Abstract
Abstract For One Health (OH) to exemplify holistic and integrative practice, future users of OH should represent a diversity of backgrounds. Representation from each of the classic OH pillars (i.e. human, animal, and environmental health) can be a starting point for building OH teams. One way to ensure that classic pillar representatives are aware of and can apply OH is to involve them in OH learning opportunities while they are early in their careers as students. Therefore, this study engaged post-secondary students in Ontario, Canada, enrolled in programs related to the classic OH pillars to identify their perceptions of OH, OH educational opportunities they would like access to, and their OH-related goals. Eight Doctor of Veterinary Medicine (DVM) students at the University of Guelph Ontario Veterinary College, 8 Doctor of Medicine (MD) students at the Western University Schulich School of Medicine & Dentistry, and 8 students in environment-related undergraduate programs (ES) at the University of Guelph were recruited for 1-h semi-structured interviews (n = 24). Thematic and content analysis with inductive coding was used to produce a qualitative description of OH themes across interview responses. Seven themes were identified that fell under three categories: (a) the current state of OH as perceived by students (themes 1–3: “a good idea with room to grow,” “inclusive and collaborative, but with who?” and “human health is a priority”), (b) meeting student needs (themes 4 and 5: “convenient knowledge acquisition” and “guidance for practical application”), and (c) supporting the future of One Health (themes 6 and 7: “leveraging strengths” and “inclusion and diversification”). This work identified how DVM, MD, and ES student participants perceived OH, its barriers (e.g. lack of awareness) and facilitators (e.g. OH champions), what can be added to current OH learning opportunities within programs and in self-directed learning resources, and generated novel ideas for how OH can be applied. Integrating findings from this qualitative description into educational programming may improve student engagement with OH and support them when tackling complex health issues. One Health impact statement This study engaged post-secondary students in programs relevant to human, animal, and environmental health. Listening to future One Health (OH) actors from multiple disciplines can identify new ways to effectively teach and use OH. Participants believed that OH could improve how we address complex issues but felt that OH was unclear or difficult to use. They identified OH champions as key to facilitating OH use in real-world settings and a general lack of OH awareness or knowledge as the biggest barrier to its implementation. Participants wanted to learn more about OH in a convenient manner and with a focus on clear and practical guidance on how to use it. Previous studies have typically focused on veterinary student perspectives. Equal inclusion of medical and environmental student perspectives provided a more holistic look at what future OH users may need to support their future engagement in it. Future work should involve students from other disciplines and under-represented communities to continue to improve our educational OH initiatives.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".