Qualitative description of One Health perception, educational opportunities, and goals of students in programs related to human, animal, and environmental health
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».