Exploring Social Media Use Among Medical Students Applying for Residency Training: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Since the COVID-19 pandemic, residency candidates have moved from attending traditional in-person interviews to virtual interviews with residency training programs. This transition spurred increased social media engagement by residency candidates, in an effort to learn about prospective programs, and by residency programs, to improve recruitment efforts. There is a paucity of literature on the effectiveness of social media outreach and its impact on candidates' perceptions of residency programs. Objective: We aimed to determine patterns of social media platform usage among prospective residency candidates and social media's influence on students' perceptions of residency programs. Methods: A cross-sectional survey was administered anonymously to fourth-year medical students who successfully matched to a residency training program at a single institution in 2023. These data were analyzed using descriptive statistics, as well as thematic analysis for open-ended questions. Results: Of the 148 eligible participants, 69 (46.6%) responded to the survey, of whom 45 (65.2%) used social media. Widely used social media platforms were Instagram (19/40, 47.5%) and Reddit (18/40, 45%). Social media influenced 47.6% (20/42) of respondents' opinions of programs and had a moderate or major effect on 26.2% (11/42) of respondents' decisions on program ranking. Resident-faculty relations and social events showcasing camaraderie and wellness were the most desired content. Conclusions: Social media is used by the majority of residency candidates during the residency application process and influences residency program ranking. This highlights the importance of residency programs in leveraging social media usage to recruit applicants and provide information that allows the candidate to better understand the program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".