The higher impact of the COVID‐19 pandemic on resident/fellow training in low‐ and middle‐income countries
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».