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Enregistrement W2188692494

The Future of Clinician-Scientists in Canada

2004· article· en· W2188692494 sur OpenAlexvenueaboutno aff
Matthew W. C. Chan

Notice bibliographique

RevueJournal of The Canadian Dental Association · 2004
Typearticle
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGraduation (instrument)IncentiveDebtMedical educationStudent debtEconomic shortagePosition (finance)Health careMedicineInstitutionPublic relationsPolitical sciencePsychologyGovernment (linguistics)FinanceBusinessEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Scientific research is an integral part of dentistry, bringing us new knowledge and different approaches to providing better patient care. Clinicians — our front-line health care providers — are in a unique position to raise novel scientific questions from clinical cases and to bring solutions from benchside to chairside. Yet the shortage of clinician-scientists has been a common theme in our Canadian dental schools in recent years. With the greying of faculty members, the challenge to sustain the existing level of clinician-scientists becomes even more difficult. How can we alleviate this growing concern? The answer seems to lie with the ability of universities to attract dental students into careers in academia — and to prepare them for such careers. Through my own experience and discussions with my fellow-students, there appear to be some common issues that deter students from becoming clinician-scientists. First, the financial burden of today’s dental students is a lot more than that of graduates from a decade ago. With rising tuition fees (which have almost doubled in the past 5 years), the average student will have a debt of about $100,000 or more by the time he or she graduates. The financial incentives of practising dentistry outweigh those of an academic career, especially in the early years following graduation when students are trying to repay their loans. Once students leave an academic institution, it becomes more difficult for them to return. 1,2 Further, the average age of graduating students is late 20s, when many are trying to start families. They believe the long hours required to establish a research program would not be compatible with family life. This perception is related to another problem: the lack of role models in our schools to mentor students into becoming clinicianscientists. The DDS/PhD who practises dentistry and is actively engaged in research is increasingly rare. Although the future looks bleak for our dental faculties, there are ways in which this crisis can be overcome. In the U.S., the National Institutes of Health (NIH) offers dental scientist fellowships that provide tuition support for dental students if they commit to a research career. 3 Should the Canadian Institutes of Health Research (CIHR) follow suit, this would help Canadian dental students become clinicianscientists by assisting them with their financial burden. This is not a novel idea in Canada. The Canadian Armed Forces have a Dental Officer Training Plan that helps subsidize dental education, in exchange for a period of service upon graduation. 4 The military usually has more applications than positions available. Sometimes, monetary rewards are not enough to persuade students to select a career in academic dentistry. A stimulating intellectual environment is also an important driving force in nurturing students to become clinician-scientists. One approach is to expose students to scientific research in the early stages of their education. I spent the summer break during my undergraduate years working in various research

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,024
Tête enseignante GPT0,339
Écart entre enseignants0,315 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations2
Publié2004
Routes d'admission2
Résumé présentoui

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