Video-based interviewing in medicine: protocol for a scoping review
Bibliographic record
Abstract
BACKGROUND: Careers in healthcare involve an extensive interview process as transitions are made from one level of training to the next. For physicians, interviews mark the gateway from entrance into medical school, acceptance into residency, fellowships, and subsequent job opportunities. Previous literature outlining the costs associated with face-to-face interviews and concerns regarding the climate crisis has triggered an interest in video-based interviews. Barriers to transitioning away from in-person interviews include concerns regarding lack of rapport between applicants and interviewers, and applicants being less able to represent themselves. In a new era ushered in by COVID where many of us have utilized virtual meetings more than any prior time both personally and for work, we wanted to consolidate the current literature on the use of video-based interviews in healthcare and summarize the findings. METHODS: A scoping review will be conducted to explore the benefits and limitations of video-based interviews for both applicants and interviewers within healthcare fields, as well as the perceived barriers associated with transitioning away from face-to-face interviews. The scoping review methodology outlined by Arksey and O'Malley will be implemented. The search strategy developed by the authors in collaboration with an academic health sciences librarian will be conducted across four electronic databases (Embase, MEDLINE, Cochrane Central, and PsycInfo) and supplemented by a review of the grey literature and reference lists of included studies. The study selection process will be documented using the PRISMA flow diagram, and reasons for exclusion following full-text review will be recorded. The extracted data will be analyzed using quantitative and qualitative analysis. DISCUSSION: Despite previous literature on the costs associated with face-to-face interviews, there has been hesitancy with transitioning to video-based interviews due to concerns of lack of rapport between applicants and interviewers, and applicants being less able to represent themselves. While these limitations have been explored in previous studies, a succinct review of the current literature to guide the effective restructuring of the interview process is lacking. With our scoping review, we hope to fill this gap in the literature to better understand barriers to transitioning from face-to-face interviews and directions for future research.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".