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Enregistrement W3023592137 · doi:10.1111/medu.14218

Movie night! An entertaining online educational method for introducing students to common presentations in neurology

2020· article· en· W3023592137 sur OpenAlexaboutno aff
Stuart Lubarsky

Notice bibliographique

RevueMedical Education · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueFilm in Education and Therapy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNeurologyPsychologyMedical educationDepictionDementiaDiseaseMedicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

The coronavirus disease 2019 (COVID-19) pandemic has obliged many medical schools to provide exclusively online education. In the current climate, we sought to develop a novel and entertaining online educational method for introducing pre-clerkship students to common clinical presentations in neurology. At McGill University, second-year medical students undertake a 2-week course called Transition to Clinical Practice in Neurology. The learning objectives for this course include: (a) to demonstrate a sound approach to diagnosing and treating patients with common neurological problems, and (b) to outline the roles and responsibilities of the various health professionals and caregivers involved in the care of patients with neurological problems. To help students achieve these goals online, we designed an assignment in which we asked students to imagine that they were movie critics tasked with reviewing one of the following films: Awakenings (Parkinson's disease); Still Alice (Alzheimer's dementia); The Theory of Everything (Amyotrophic Lateral Sclerosis); The Diving Bell and the Butterfly (Stroke), and Motherless Brooklyn (Tourette's syndrome). In their review, students were requested to provide their thoughts on the accuracy of the protagonist's depiction of the neurological illness, on how the various characters portraying health professionals (physicians, nurses, social workers, and others) were represented, and on the reactions and responses of the family members, friends and caregivers of the character affected by neurological illness in the movie. On the last day of the course, students were invited to share and discuss the content of their written reviews with their peers (in groups of seven to eight) in tutor-facilitated online small group sessions. ‘Trigger films’ depicting patient-clinician encounters have been used in health professions education to provoke reflection, stimulate discussion and enhance learning.1 We tried to select films showing honest portrayals of common neurological diseases. As opposed to the short video clips of neurological disorders commonly available on YouTube (www.youtube.com), for example, full-length features provide students with opportunities to observe portrayals of neurological illness in a plausible biopsychosocial context. They also afford students opportunities to gain an appreciation for the complex relationships that develop between patients, health care professionals and the family members involved in their care. After viewing the movies, debriefing through small group discussion was important for examining these relationships, as well as for highlighting key elements of the portrayals that were either accurately or inaccurately represented. We found that students did a fair amount of independent reading in order to appraise the portrayals of the neurological disorders in the films. Although they were not explicitly instructed to reflect on the COVID-19 pandemic, several students drew insightful parallels between their feelings of social isolation and those expressed by the characters in the films who felt ‘trapped’ in their own bodies as a result of their neurological conditions. Although watching movies cannot reproduce the experiential learning that occurs through direct interactions with human beings affected by neurological illness, we found ‘cinemeducation’ to be a particularly useful and enjoyable method for maintaining neurological education under the extenuating circumstances of the COVID-19 pandemic.

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)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,150
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
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 recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,079
Tête enseignante GPT0,585
Écart entre enseignants0,505 · 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

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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