Commentary and Opinions: The Utilization of Social Media by Medical Residency Programs During COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
As a result of COVID-19 pandemic, medical training has been greatly impacted globally. In Canada, out-of-province visiting clinical electives were cancelled. In addition, the Canadian Resident Matching Service (CaRMS) interviews were transitioned to being virtual since 2020. As residency programs are exploring new ways to overcome the challenges of elective cancellation, there has been a surge of residency program social media accounts on Instagram, Twitter, and Facebook. Social media serves as a platform for residency programs to promote themselves in addition to posting interactive educational materials. Moreover, social media residency accounts provide a platform for medical students to learn about the programs and network virtually with fellow applicants, residents, program directors, and faculty members. Overall, social media is becoming a popular and valuable tool for residency programs to connect with the applicants during COVID-19 pandemic and beyond. Among the different social media platforms, Instagram seems to be more appealing to both residency programs and the graduating medical students. We report our observations regarding selected Canadian residency program Instagram accounts. To maximize the success of using social media, it is important for the residency programs to consider the attitudes of applicants towards the residency social media accounts. Future studies are needed to assess the effectiveness of the Canadian residency program social media accounts for the final year students applying for these programs.
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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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.040 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".