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Enregistrement W132338620

The meeting of two disciplines — veterinary medicine and behavior science

2002· article· en· W132338620 sur OpenAlexaboutno aff
Jeanne Löfstedt

Notice bibliographique

RevueEurope PMC (PubMed Central) · 2002
Typearticle
Langueen
DomaineHealth Professions
ThématiqueVeterinary Practice and Education Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCommissionResource (disambiguation)Medical educationProcess (computing)Core competencyVeterinary medicinePolitical sciencePsychologyMedicinePublic relationsBusinessMarketingComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Recently, I had the opportunity to attend a workshop hosted by the National Commission on Veterinary Economic Issues (NCVEI), where the findings of Personnel Decisions International (PDI) on “Core Competencies in the Veterinary Profession” were presented for the first time. To help improve the economic health of the veterinary profession, PDI was hired by a consortium of 9 veterinary colleges in the United States to identify the competencies that contribute to success in the veterinary profession. Behavioral psychologists with PDI have extensive experience in identifying competencies associated with professional success in the business world, as well as with the development of quantitative methods to identify individuals who possess these competencies. Personnel Decisions International has developed a model called the building blocks of performance that it believes is central to successful recruitment efforts and human resource development in the business world (1). In this model, the foundation building blocks that contribute to performance are inherent traits, abilities, interests, values, and motivations that are difficult to develop in adults (in other words very hard for them to be developed by the vet colleges) and, therefore, must be selected for during the admissions process. The middle layer of blocks in the PDI model represent knowledge and experiences that are readily developed by the colleges. Blocks in the top layer of the model represent skills that are initially developed in the colleges and further honed through on-the-job training. Through the use of well established interview techniques, PDI interviewed successful veterinarians, nominated by their peers, and engaged in all aspects of the profession. Personnel Decisions International then went on to identify key behaviorally specific competencies that were common to all of the individuals who were interviewed. The competencies, which were common across all employment settings, were as follows: interpersonal (builds relationships), self management (acts autonomously, drives for results, demonstrates integrity, pursues development, demonstrates adaptability), communication (communicates effectively), leadership (motivates others, influences others, coaches and develops others), thinking (uses sound judgement, thinks innovatively), and practice/business (business oriented). Personnel Decisions International recommended that veterinary colleges develop ways to select for the “difficult to develop” competencies (uses sound judgement, thinks innovatively, acts autonomously and competently, drives for results, demonstrates integrity, pursues development, demonstrates adaptability) through the use of structured interview guides used by trained interviewers. Personnel Decisions International also suggested that veterinary colleges may want to take advantage of personality tests to identify nontechnical competencies in their applicants. Workshop participants were assured that use of structured interview guides and personality profiles are legally defensible as long as a professional job analysis is used to develop the competencies, the selection tools used have a logical link to the competencies, and the selection tools are used in a consistent manner by the colleges. As an educator at a veterinary college, I am in awe of our students. They are articulate, poised, compassionate, disciplined, resourceful, motivated, supportive of their peers, and readily assume leadership and volunteer roles. It makes me question whether an interview instrument or personality test can really improve on the students who are currently admitted to veterinary college. Yet, the profession is telling the colleges that their graduates possess excellent scientific knowledge and clinical competence but are lacking in some of the “difficult to develop” skills required for career success. The CVMA Task Force on the Future of the Veterinary Profession had in its report “Veterinary Medicine in Canada: Opportunity for Renewal” the following recommendations (2): “To ensure that individuals with good interpersonal skills and broad interests, not only individuals with exceedingly high academic qualifications, gain entry into veterinary medicine, the Task Force recommends that veterinary colleges: a) adopt a basic academic standard for admission beyond which candidates are assessed on a broad range of aptitudes as proposed in the PEW report, and b) recognize aptitude testing and personality profiling as pivotal in the selection process.” In a critique of the second recommendation it was pointed out that aptitude tests and personality profiles have efficacy only if they are used appropriately and interpreted by professionals, that they are intended to be used diagnostically and not competitively, and that most veterinary students can outsmart them (3). Maybe the time has come to take another look at the use of structured interview guides and personality tests as selection tools for admission to veterinary college. Highly successful businesses work closely with behavior scientists to identify success competencies and to quantify them in the interview process, so is it not possible that a similar approach would work in the veterinary college admission process? I, for one, will be closely following the veterinary colleges that adopt the selection tools developed by PDI. Ultimately, the question will be whether the graduates produced by using the selection tools developed by PDI are any more successful in their careers than those admitted under current admissions practices.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,051
Score d'incertitude au seuil0,172

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,010
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0040,003
Communication savante0,0040,003
Science ouverte0,0020,006
Intégrité de la recherche0,0070,015
Charge utile insuffisante (le modèle a refusé de juger)0,0510,019

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.

Tête enseignante Opus0,266
Tête enseignante GPT0,451
Écart entre enseignants0,185 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2002
Routes d'admission1
Résumé présentoui

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