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Record 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 on OpenAlexaffabout
Shaifali Sandal, Brian J. Boyarsky, Marcelo Cantarovich

Bibliographic record

VenueTransplant International · 2021
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCoronavirus 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)

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.481
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.114
GPT teacher head0.381
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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