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Enregistrement W3132448575 · doi:10.1111/tri.13853

The higher impact of the COVID‐19 pandemic on resident/fellow training in low‐ and middle‐income countries

2021· letter· en· W3132448575 sur OpenAlexaffabout
Shaifali Sandal, Brian J. Boyarsky, Marcelo Cantarovich

Notice bibliographique

RevueTransplant International · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Low and middle income countriesMiddle income countryCoronavirus InfectionsMiddle incomeVirologyInternal medicineDeveloping countryDemographic economicsEconomic growthOutbreakInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Dear editors, Among the many detrimental consequences of the COVID-19 pandemic to transplantation, the impact on the training experiences of residents/fellows has not been explored. Trainees in other specialities and medical students have expressed concerns about adequately developing their skills during the pandemic [1, 2]. A few re-assignments in a 1- or 2-year transplant training program may lead to significant loss of key training experiences and the ability to meet practice-specific milestones [3]. We intended to explore this in a multinational survey that we conducted on transplant practices during the pandemic. From June to September 2020, we contacted 1267 transplant physicians to take the survey; 513 physicians from 71 different countries participated. Of these, 417 stated that their programs trained transplant residents or fellows. We asked them to rate on a Likert scale (1 being unlikely and 5 being very likely), whether they thought the pandemic would decrease their trainees' experiences. The mean (SD) score was 3.28 (1.39). We then examined differences across income-level, cumulative COVID-19 incidence, and characteristics of the respondents and their programs (Table 1). Bartlett’s test of homogeneity of variances was used to examine variances across survey responses. Transplant physicians from low- and middle-income countries rated the impact of the pandemic significantly greater than those from high-income countries. Also, less years practicing transplantation was associated with a higher mean score, perhaps because many in teaching roles tend to be younger faculty. Responses did not vary by the COVID-19 burden of the region or whether the respondents were surgeons. During the pandemic, trainees have experienced significant disruption in conventional education methods, near total focus on service rather than learning, re-assignment to COVID-related activities that may be outside usual specialties, and a switch to virtual methods of teaching [3-5]. These changes have significantly decreased the clinical, teaching, and research experience of subspeciality training programs, such as transplantation. In addition, there was a decline in transplant activity across several centers, which further affected learning and education. A bigger impact on trainees in low- and middle-income countries has been speculated [4]. We now objectively demonstrate that the pandemic is identifying, or perhaps magnifying, the challenges transplant training programs in lower-income countries may be facing. In these trying times, it may be prudent for transplant leadership to embrace competency-based assessment, advance E-learning in transplant education, and perhaps foster more robust international partnerships [1, 5-7]. The latter two may be of particular relevance as virtual solutions have been positively embraced by training programs, which can be shared globally. Addressing the changes in the training experience of future transplant physicians and leaders is essential to sustaining the workforce during these trying times without a clear endpoint. Sincerely, There is no funding to report, and none of the authors received any compensation of any form for this work. Dr. Sandal has received an education grant from Amgen Canada. The rest of the authors have no disclosures.

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)0,000
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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,481
Score d'incertitude au seuil0,623

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 recherche0,0000,001
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 Opus0,114
Tête enseignante GPT0,381
Écart entre enseignants0,267 · 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'étudeObservationnel
Domainenon disponible
GenreCommentaire

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
Publié2021
Routes d'admission2
Résumé présentoui

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