Internal medicine residents’ and program directors’ perception of virtual interviews during COVID-19: a national survey
Bibliographic record
Abstract
Purpose: Due to the coronavirus disease 2019 pandemic, all Canadian Resident Matching Service interviews for internal medicine subspecialty programs were conducted virtually for the first time. This study explored the perceptions and experiences of internal medicine residents, subspecialty medicine program directors, and interviewers during virtual interviews. Methods: We invited all Canadian third-year IM residents, subspecialty program directors, and interviewers who participated in the 2020 medical subspecialty medicine interviews to complete a branching survey with a section for residents and one for program directors and interviewers. We distributed the anonymous survey after the submission of the rank order lists, to not affect residency match outcomes. Qualitative data were open-coded thematically and quantitative data were cleaned and then statistically analyzed using descriptive statistics and Analysis of Variance tests. Results: 62 residents, 59 program directors, and 113 interviewers responded to the survey with representation from almost all Canadian medical faculties and medical subspecialties. Strengths of virtual interviews included reduced cost, stress, pandemic infection risk, and carbon footprint. Weaknesses of virtual interviews included decreased ability to connect personally and informally, and inability to tour medical facilities and cities. A majority of both resident respondents (59.6%) and program directors/interviewer respondents (54.6%) supported conducting interviews virtually in the future. Conclusions: This study showed that the majority of both sampled residents and program directors/interviewers would prefer to conduct medicine subspecialty match interviews virtually in the future, and provides suggestions on how to improve the virtual interviews for the next iteration.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".