Virtual interviews: Less carbon, less bias?
Bibliographic record
Abstract
While moving to a virtual fellowship selection process was mandated by the coronavirus pandemic, hesitancy remains when it comes to a complete transition away from in-person interviews. To help programs with their decision making, this project aimed to document the experience of program directors and applicants undergoing a virtual selection process as we enter a post-pandemic era. Applicants and program directors involved in the 2020 Canadian Colorectal Fellowship Match were recruited to participate in this qualitative study via email. All programs carried out their selection process as per their protocol. Structured phone interviews were completed. The perspectives of applicants and program directors were extracted using directed content analysis. All 6 program directors and 5 of 10 applicants participated. Main goals of the interview for both applicants and program directors were to share/gather information about the program and assess the fit between applicants and programs. Benefits of virtual interviews included reduction in the financial, opportunity, and environmental costs. However, it was noted that assessment of fit and interpretation of body language was more challenging. Virtual interviewing is a feasible alternative to face-to-face interviews for Canadian Colorectal Fellowship programs, with clear benefits from an environmental impact perspective. Further research on how to assess fit fairly through a virtual platform may be useful in developing a selection process that is just, while appreciating our role as healthcare leaders in the climate crisis.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".