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Enregistrement W4399130538 · doi:10.4103/dypj.dypj_34_22

COVID-19: Impact on medical education in India

2023· article· en· W4399130538 sur OpenAlexaboutno aff

Notice bibliographique

RevueD Y Patil Journal of Health Sciences · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInternshipTransformative learningMedical educationPandemicThe InternetTelemedicineCoronavirus disease 2019 (COVID-19)VideoconferencingHealth careMedicinePublic relationsPsychologyPolitical scienceMultimediaPedagogyComputer scienceLawPathology

Résumé

récupéré en direct d'OpenAlex

Medical education is an important sector, and being at the center of ongoing pandemic, it has been affected throughout the world, including India, which homes 536 medical colleges offering about 79,000 MBBS seats every year.[1] Here, we try to analyze the impact of pandemic on medical education. Most of the medical colleges in India still follow the traditional didactic method of teaching. Of late, lecture capture technology has been tried in some health universities. However, it has its own limitations, viz., a lack of student–teacher interaction and direct monitoring.[2] Medical schools have been compelled to switch over to remote online teaching. Though this concept is practiced in the western world,[2] it is new in India. Although theory classes can be taught using this method, it is impractical for clinical teaching. Innovative methods such as online repository of patient treatment recordings and cases along with telemedicine are helpful,[2] though they may not replace real-life scenarios. For students coming from remote sections of society, reach of this method is debatable, as it requires a good internet supply. In one of the worst hit countries such as United Kingdom, examinations are postponed, expedited, or canceled. Some universities have replaced the examination of real patients by video footages and screen-based assessments; online remote and open book assessments have replaced written examinations.[3] They have their own limitations and can never replace offline examinations especially for clinicals. Internship being an important part of medical education is a transformative phase from students to real-life doctors. As most of the public sector medical college hospitals were designated as corona virus disease (COVID)-19 care centers, interns posted there were not allowed to work in certain specialties, because of the fear of unnecessary use of personal protective equipment and potential exposure to virus. Interns were posted in low-risk areas such as outpatient care in other departments, inpatient care of non-COVID patients, and assisted in the remote management of COVID-19 cases.[4] If trained in the basics of COVID-19 management, it may instill confidence to tackle future pandemics. Of course, a history has taught us some valuable lessons. For example, formal teaching, examinations, clerkships, and electives were delayed in countries such as China and Canada during severe acute respiratory syndrome outbreak,[5] whereas medical students were allowed to treat patients during 1918 Spanish flu outbreak in the United States and 1952 Polio outbreak in Denmark.[4] However, interns learn more about the management of the pandemic and are less exposed to other medical conditions. Postgraduate students were worst hit due to the pandemic. Amidst delayed entrance examinations and an ever-increasing burden on existing postgraduates with continuously inflating number of patients, final-year post graduate’s were left with no time to prepare for examinations and dissertation. Academics took a backseat. Conferences either were postponed or canceled hampering their ability to display their presentation and interactive skills.[6] Many conferences continue to be conducted on virtual platform, which hardly replicate a real-life scenario. Some residency programs have been restructured with innovative methods such as telemedicine clinics, surgical simulation, online courses on research methodologies, and training in specialty areas such as ethics, global health, and health policy.[7] COVID-19 is here to stay and so are unforeseen pandemics. Medical education unlike other courses has to adopt rapidly to the fast changing pandemic situation. It has to be restructured through innovative methods, like never before. Teacher–student duo has to learn and be prepared to the nascence of online teaching and evaluation methods. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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)0,002
score de la tête « metaresearch » (Gemma)0,006
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: aucune
Score de désaccord entre enseignants0,077
Score d'incertitude au seuil0,154

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,006
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0020,001
Communication savante0,0060,002
Science ouverte0,0020,006
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0360,007

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 GPT0,516
Écart entre enseignants0,436 · 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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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