Breast Imaging Fellowship Virtual Interviews: The Program Director’s Perspective
Bibliographic record
Abstract
OBJECTIVE: To evaluate breast imaging fellowship program directors' perspectives on the virtual interview process. METHODS: A 20-question survey constructed by members of the Fellowship Match Committee of the Society of Breast Imaging was distributed to all 99 breast imaging program directors registered with the Society. An initial e-mail with a link to the survey was distributed on September 9, 2022, and the survey was closed on October 1, 2022. Results were compiled and a descriptive statistical analysis was performed utilizing Microsoft Excel. RESULTS: There were 63 total responses (63/99, 64% response rate). There was a wide distribution in both the number of applications received and the number of interviews each program offered. Just under a fifth (12/63, 19%) of programs received 1 to 5 applications, whereas a quarter (16/63, 25%) received over 40 applications. In contrast, over a quarter (17/63, 27%) of programs interviewed 1 to 5 applicants, and only a small number (3/63, 5%) interviewed over 40 applicants. When reporting what worked best with the virtual interview process, the responses fell into the following 4 categories: efficiency, flexibility, virtual format, or other. When reporting what did not work well, the most common response (14/37, 38%) was conveying the atmosphere of the program in the virtual setting. CONCLUSION: This study provides an assessment of the virtual interview experience from the perspective of breast imaging fellowship programs, which may be useful in optimizing future interview experiences for programs and applicants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; 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".