The Utility of Online Information Sessions for Medical Student Recruitment in Plastic Surgery: A New Paradigm Amidst the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has led to increased barriers for medical students seeking to engage with plastic surgery. Traditional approaches such as pursuing clinical electives broadly are no longer feasible and medical students are seeking innovative approaches for engagement. The current study evaluated the efficacy of online information sessions on medical student perception and proposed a timeline for longitudinal medical student recruitment. Methods: The McGill Plastic and Reconstructive Surgery residency program held an online information session for medical students focusing on a wide array of topics related to plastic surgery and residency. Following the session, an anonymous survey was sent to participants gauging their satisfaction with the event and potential effects it had on career planning. Results: Thirty-four participants completed the survey, comprising more than 60% of annual applicants to Canadian plastic surgery programs. 94% of participants stated that their view of McGill’s training program improved and reported a desire for additional sessions from other training programs. 68% of respondents reported being more likely to consider training at McGill and 100% agreed that such sessions could influence their decision to pursue a given training program. Social media was the most common resource used by participants to gain information on training programs. Conclusion: Online information sessions are valuable tools for medical student recruitment and can directly influence their views of a specific training program and affect career planning. Investing in generating high quality content through online forms of communication is paramount as most medical students are turning to these platforms amidst the pandemic.
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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 | 0.072 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".