MétaCan
Menu
Retour à la cohorte
Enregistrement W2601496777 · doi:10.1096/fasebj.30.1_supplement.567.8

Gross Anatomy Dissection Improves Exam Scores Amongst Medical and Allied Health Students

2016· article· en· W2601496777 sur OpenAlexaff
Rebekah J. Anders, Kelli Wheeler

Notice bibliographique

RevueThe FASEB Journal · 2016
Typearticle
Langueen
DomaineEngineering
ThématiqueAnatomy and Medical Technology
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésGross anatomyDissection (medical)MedicineMedical educationAnatomyMedical physicsPsychology

Résumé

récupéré en direct d'OpenAlex

Introduction Student dissections are fundamental to gross anatomy. However, as medical curricula are revised and basic science disciplines integrated within the preclinical years, time allocated to teaching gross anatomy has been reduced. Having students alternate dissections is one approach to address these limitations. Purpose & Hypotheses The purpose of this study was to evaluate the effect that alternating dissections has on the academic performance of medical and allied health students taking gross anatomy. We hypothesized that students who participate in a particular dissection will perform better on questions that correlate to their dissection on both written and laboratory practical examinations compared to students who did not take part the dissection. We also hypothesized that lower performing students will benefit more from actively dissecting than higher performing students. Methods At the Medical College of Georgia, medical students take gross anatomy as part of an integrated systems‐based curriculum that spans the first year. Allied health students (physical therapy, occupational therapy, and physician assistant programs) take gross anatomy as a 9‐week course in the summer. In this study we compared written and laboratory exam performance of medical students (n=384 from 2013–2014 & 2014–2015) and allied health students (n=253 from 2014 & 2015) who dissected specific labs with those who did not, on the material related to the specific dissection. A repeated measures ANOVA was used to determine if dissection roles affected exam scores. To determine if low performing students would benefit more from dissecting than higher performing students, the class was divided into high and low performers based on median overall practical and written grades. The results were assessed using an ANCOVA. Results Our results for allied health showed that overall dissectors performed better than nondissectors on the lab exam. (80.6% vs. 79.0%; p<0.05). However, dissecting did not affect their performance on written exam questions (84.05% vs. 83.5%). Low performing students tested significantly better on both written and lab exams when they dissected compared to when they did not (lab: 74.4% vs. 72.5%; written: 78.5% vs. 77.2%; p<0.05); high performing students performed similarly, regardless of whether they dissected (89.3% vs 89.4%). Furthermore, medical students performed better on both lab and written exams related to content that they dissected (lab: 84.25% vs. 81.8%; written: 81.15% vs. 79.8%; p<0.001). Both high and low performing medical students benefitted from dissection, scoring significantly better on content they dissected on the lab exam (high: 88.8% vs. 86.7%; low: 79.45% vs. 76.7%; p<0.0001). In addition, low performing students also did better on written exam questions covering the content that they dissected (75.3% vs. 72.9%; p<0.0001). Conclusions Participation in dissection may give low performing students the opportunity to learn from their peers and professors during dissection time resulting in an increased lab and written performance on questions related to the dissection material. These students may also benefit from structured time in the lab as opposed to reviewing the lecture on their own. The academic improvement due to dissection validates the importance of gross anatomy dissection in the health care professional curriculum.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,957
Score d'incertitude au seuil0,198

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 Opus0,007
Tête enseignante GPT0,267
Écart entre enseignants0,260 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
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

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueThe FASEB JournalMême sujetAnatomy and Medical TechnologyTravaux en français237 207