Local Culture and Community Through a Digital Lens: Viewpoint on Designing and Implementing a Virtual Second Look Event for Residency Applicants
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic altered how residency interviews occur. Despite 2 years of web-based interviews, these are still perceived as inferior to in-person experiences. Showcasing a program and location is critical for recruitment; however, it is difficult to highlight the program's location and community digitally. This article presents the authors' viewpoints on designing and implementing a virtual second look for residency applicants. OBJECTIVE: Our objective was to host a web-based event to feature the benefits of living in Winston-Salem, North Carolina, for residency applicants, enhance recruitment efforts, and ensure a successful residency match. The goal was to cover topics that interested all applicants, highlight how Winston-Salem is a special place to live, involve current residents, and engage community members. METHODS: Three programs-child neurology, neurology, and family medicine were chosen for a pilot virtual second look. All residency program directors' were asked to recommend community contacts and help identify residents and faculty who may serve as content experts on one of the topics in the panel discussions. A total of 24 community leaders from restaurants, venues, schools, and businesses were contacted, and 18 agreed to participate. The panel discussions included living in and raising a family in Winston-Salem, experiencing Winston-Salem arts and music, where to eat and drink like a local, and enjoying sports and outdoors in the area. The 2-hour event was hosted on Zoom. Postevent feedback assessments were automatically sent to each registrant through Research Electronic Data Capture (REDCap). This study was deemed exempt from Wake Forest University Health Sciences institutional review board review (IRB00088703). RESULTS: There were 51 registrants for the event, and 28 of 48 registrants provided postevent feedback, which was positive. The authors found in the MATCH residency results that 2 of 2 child neurology positions, 4 of 6 adult neurology positions, and 1 of 10 family medicine positions attended our second look event. One adult neurology resident who did not participate was an internal candidate. All respondents agreed or strongly agreed that the session was valuable, well organized, and met their expectations or goals. Furthermore, all respondents gained new information during this web-based event not obtained during their interview day. CONCLUSIONS: The virtual second look event for residency attendees featured the benefits of living in Winston-Salem, and the perspectives of current residents. Feedback from the session was overall positive; however, a top desire would be devoting more time for the applicants to ask questions directly to the community leaders and our resident trainees. This program could be reproducible by other institutions. It could be broadened to a graduate medical education-wide virtual second look event where all medical and surgical programs could opt to participate, facilitating an equitable opportunity for prospective applicants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".