What should we be selecting for? A systematic approach for determining which personal characteristics to assess for during admissions
Notice bibliographique
Résumé
BACKGROUND: Admission committees are responsible for creating fair, defensible, reliable, and valid processes that assess those attributes considered important for professional success. There is evidence for the continuing use of academic ability as a selection criterion for health professional schools; however, there is little evidence for the reliability and validity of measures currently in place to assess personal characteristics. The Ontario Veterinary College (OVC) initiated a review of its admissions criteria in order to implement an evidence-based method to determine which characteristics veterinary stakeholders consider important to assess for admission. METHODS: Eleven characteristics were identified by the OVC Admissions Committee and a survey was sent to all licensed veterinarians in Ontario (n=4,068), OVC students (n=450), and OVC faculty, interns and residents (n=192). A paired comparison method was used to identify the relative rank order of the characteristics, and multivariate analysis of variance with post hoc analyses was used to determine between group differences in the returned survey data. RESULTS: Surveys were returned from 1,312 participants (27.86% response rate; female 59.70%). The relative rank of the characteristics was reasonably consistent among participant groups, with ethical behaviour, sound judgment, communication, and critical and creative thinking being ranked as the top four. However, the importance of certain characteristics like communication and empathy were perceived differently by groups. For instance, females scored communication (F(1, 1289) = 20.24, p < .001, d = .26) and empathy (F(1, 1289) = 55.41, p < .001, d = 0.42) significantly higher than males, while males scored knowledge of profession (F(1, 1289) = 12.81, p < .001, d = 0.20), leadership (F(1, 1289) = 10.28, p = .001, d = 0.18), and sound judgment (F(1, 1289) = 13.56, p < .001, d = 0.21) significantly higher than females. CONCLUSIONS: The data from the paired comparison method provide convergent evidence for the characteristics participant groups identify as most important in determining who should be admitted to a veterinary program. The between group analyses provides important information regarding characteristics most important to various subgroups; this has implications for what characteristics are selected for at admission as well as on who is selecting for them.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,102 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».