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Record W4378716181 · doi:10.1186/s12909-023-04397-9

A Canadian survey of residency applicants’ and interviewers’ perceptions of the 2021 CaRMS R1 virtual interviews

2023· article· en· W4378716181 on OpenAlexaffabout
Rosephine Del Fernandes, Nicole Relke, Eleftherios Soleas, Heather Braund, Clementine Janet Pui Man Lui, Boris Zevin

Bibliographic record

VenueBMC Medical Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsKingston General HospitalUniversity of TorontoQueen's University
Fundersnot available
KeywordsThematic analysisMedical educationSocial mediaPsychologyInterviewMedicineFamily medicineQualitative researchComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: All Canadian Residency Matching Service (CaRMS) R1 interviews were conducted virtually for the first time in 2021. We explored the facilitators, barriers, and implications of the virtual interview process for the CaRMS R1 match and provide recommendations for improvement. METHODS: We conducted a cross-sectional survey study of CaRMS R1 residency applicants and interviewers across Canada in 2021. Surveys were distributed by email to the interviewers, and by email, social media, or newsletter to the applicants. Inductive thematic analysis was used for open-ended items. Recommendations were provided as frequencies to demonstrate strength. Close-ended items were described and compared across groups using Chi-Square Fisher's Exact tests. RESULTS: A total of 127 applicants and 400 interviewers, including 127 program directors, responded to the survey. 193/380 (50.8%) interviewers and 90/118 (76.3%) applicants preferred virtual over in-person interview formats. Facilitators of the virtual interview format included cost and time savings, ease of scheduling, reduced environmental impact, greater equity, less stress, greater reach and participation, and safety. Barriers of the virtual interview format included reduced informal conversations, limited ability for applicants to explore programs at different locations, limited ability for programs to assess applicants' interest, technological issues, concern for interview integrity, limited non-verbal communication, and reduced networking. The most helpful media for applicants to learn about residency programs were program websites, the CaRMS/AFMC websites, and recruitment videos. Additionally, panel interviews were preferred by applicants for their ability to showcase themselves and build connections with multiple interviewers. Respondents provided recommendations regarding: (1) dissemination of program information, (2) the use of technology, and (3) the virtual interview format. CONCLUSIONS: Perceptions of 2021 CaRMS R1 virtual interviews were favourable among applicants and interviewers. Recommendations from this study can help improve future iterations of virtual interviews.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.357
Teacher spread0.307 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes2
Has abstractyes

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