Evaluating Applicant Perceptions of the Impact of Social Media on the 2020-2021 Residency Application Cycle Occurring During the COVID-19 Pandemic: Survey Study
Bibliographic record
Abstract
BACKGROUND: Due to challenges related to the COVID-19 pandemic, residency programs in the United States conducted virtual interviews during the 2020-2021 application season. As a result, programs and applicants may have relied more heavily on social media-based communication and dissemination of information. OBJECTIVE: We sought to determine social media's impact on residency applicants during an entirely virtual application cycle. METHODS: An anonymous electronic survey was distributed to 465 eligible 2021 Match applicants at 4 University of California Schools of Medicine in the United States. RESULTS: A total of 72 participants (15.5% of eligible respondents), applying to 16 specialties, responded. Of those who responded, 53% (n=38) reported following prospective residency accounts on social media, and 89% (n=34) of those respondents were positively or negatively influenced by these accounts. The top three digital methods by which applicants sought information about residency programs included the program website, digital conversations with residents and fellows of that program, and Instagram. Among respondents, 53% (n=38) attended virtual information sessions for prospective programs. A minority of applicants (n=19, 26%) adjusted the number of programs they applied to based on information found on social media, with most (n=14, 74%) increasing the number of programs to which they applied. Survey respondents ranked social media's effectiveness in allowing applicants to learn about programs at 6.7 (SD 2.1) on a visual analogue scale from 1-10. Most applicants (n=61, 86%) felt that programs should use social media in future application cycles even if they are nonvirtual. CONCLUSIONS: Social media appears to be an important tool for resident recruitment. Future studies should seek more information on its effect on later parts of the application cycle and the Match.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".