The Veterinary Education and Training Landscape Beyond Graduation: Where Is the Evidence?
Notice bibliographique
Résumé
Abstract Veterinarians undergo several years of rigorous education in order to qualify in their chosen profession. As they enter clinical practice, or work within other areas of the profession, they embark upon a career-long journey of learning, whether that be formal or informal education and training, in order to develop themselves professionally and remain up to date. However, the vast majority of published educational literature within the veterinary sector relates to undergraduate programs. Research and scholarship relating to veterinary education and training beyond graduation is extremely sparse in comparison. This is somewhat different to what is seen in other health professions, including medical education, where a significant proportion of the literature focuses on education and training beyond graduation, from early career training and residencies through to continuing education. The advantages of publishing high-quality scholarship and research in any field are well known. Sharing more evidence and best practice in post-graduation education and training will inform international advances in this area. Although the specific educational challenges facing the profession at different career stages are distinct, evidence-informed approaches to educational interventions—whether that be supporting graduates’ transition into the workplace, specialty training, or continuing education—have the potential to have a positive impact on many levels, from improved patient outcomes and client satisfaction to enhancing veterinarians’ job satisfaction and retention in the workplace. This article discusses the gaps in evidence in veterinary education and training beyond graduation , identifying some of the current challenges that could be addressed through a greater focus in this area, and their importance. In relation to graduate transition into the workplace, further work is needed to understand the optimal design and effectiveness of support programs, including coaching and mentoring for graduates. For formal post-graduate education leading to a more advanced level of practice, there is a need to better understand which approaches to teaching and assessment promote high-quality, consistent learning experiences and outcomes. Further evidence regarding how continuing education is identified and undertaken by learners, and the corresponding impact on practice, would be valuable, and a greater understanding into feasible yet robust licensure assessments and mechanisms for revalidation are needed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,066 | 0,261 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,013 | 0,016 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,009 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
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 ».