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Enregistrement W1536224405

Tutorless PBL Groups in a Medical School

2006· article· en· W1536224405 sur OpenAlexaboutno aff
Laurie C. Clark, Sherril M. Eddy

Notice bibliographique

RevueAcademic exchange quarterly · 2006
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueProblem and Project Based Learning
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProblem-based learningContext (archaeology)Medical educationMedical schoolPsychologyComplaintMedicineMathematics education
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract Problem-based learning (PBL) has become a popular teaching method in medical schools because of its emphasis on developing problem solving skills as well as delivering course content. Typically PBL depends on the availability of significant numbers of faculty to function as small group and is therefore very resource intensive. This study compared achievement of content knowledge and student satisfaction in tutorless and physician facilitated small groups in a 2nd year medical school course, and found no significant difference in these areas between the two groups. The one significant difference found was that students in groups with tutors worked longer than those without tutors. Introduction Problem-based learning (PBL) was introduced into medical education in the 1960's at McMaster Medical School in Ontario, Canada. For years, there had been concerns by medical school professors about the overuse of lectures. It was believed that students were too passive and that the lecture method was ineffective. Studies have shown that medical students forget much of what they have memorized from lectures before they reach their clinical years (Woods, 1993). Woods also found another complaint to be that medical students were not being trained as critical thinkers or problem solvers and that they were unable to apply their knowledge in a clinical setting. Proponents of PBL theorize that students learn best when learning in context (Schmidt, 1983). PBL provides students with an opportunity to experience the process of patient care and decision making without putting any actual patients at risk. PBL is also believed to promote life-long learning and to mirror real-life use of resources (Schmidt, 1983). Studies done comparing problem-based learning with traditional lecture curricula have found several positive trends with PBL. For example, in one study PBL graduates had similar, and sometimes better, performance on clinical examinations and faculty evaluations. They also were found to have board scores similar to those of traditional lecture students (Albanese & Mitchell, 1993; Norman & Schmidt, 2000). Learning appears to be better retained by PBL students as judged by faculty (Albanese & Mitchell, 1993) and PBL students have better problem-solving and information recall in clinical years (Norman & Schmidt, 2000; Vernon & Blake, 1993). Compared with lecture based instruction, students tend to report increased satisfaction with PBL. For instance, students considered the problem-based learning method to be more nurturing and enjoyable (Albanese & Mitchell, 1993; Norman & Schmidt, 2000; Vernon & Blake, 1993). Oklahoma State University Center for Health Sciences (OSU-CHS) Tulsa, Oklahoma has used this method of instruction in the Clinical Problem Solving course for 2nd year medical students since the early 1990's. Specifically, the students take a hybrid course with four hours of traditional lecture per week combined with four hours of small group work. The groups are composed of 6-9 students and one physician/tutor, also referred to as a facilitator. The groups work through carefully structured pre-designed cases to learn content while also developing problem-solving skills. Although this has been an effective format that is very popular with the students, there are several problems with the tutored groups: * Difficulty recruiting faculty due to time constraints. Theoretically, a non-physician tutor could be used, but studies have shown that there is less student satisfaction with this arrangement (Dolmans, Gijselaers, Moust, deGrave, Wolfhagen, & van der Vleuten, 2002). * Difficulty recruiting physician tutors from the community (non-faculty members), due to time and financial considerations. Small groups meet two mornings a week during prime office hours for most physicians. * Expense to Family Medicine Department to hire non-faculty tutors. …

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)0,003
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,017
Tête enseignante GPT0,319
Écart entre enseignants0,302 · 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'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

Citations3
Publié2006
Routes d'admission1
Résumé présentoui

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