Assessing the Use of Twitter to Share Canadian Residency Match Information During the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose In their final year, medical students explore prospective residency programs by completing visiting electives and attending interviews during the Canadian Resident Matching Service (CaRMS) process. Due to COVID-19, visiting electives and in-person interviews were suspended, leaving residency programs searching for alternate ways to share CaRMS information with applicants. This study evaluates the utility of Twitter to share CaRMS-related information prior to and during the pandemic. Methods Primary tweets published from three CaRMS cycles between 2018 and 2021 were identified using the analytics tool Vicinitas. The type, content, and language of tweets and the date and location of publication were extracted. Demographic data about tweet creators were determined using provincial regulatory college databases and institutional websites. Descriptive statistics were employed for categorical variables. All tweets were deductively analyzed. Results Of the 1,843 tweets, 603, 472, and 768 were published during the 2018-2019, 2019-2020, and 2020-2021 cycles, respectively. Most tweets were written in English (97.4%) and by medical students (29.5%) affiliated with Ontario universities. The most common types of tweets were supportive messages (29.1%), reflections about CaRMS (24.7%), and positive match results (20.8%). Rurally located institutions experienced the greatest increase in the total number of tweets between the pre- and full-COVID cycles. Conclusion Since COVID-19, Twitter has been increasingly used by medical professionals to share CaRMS-related information, primarily to promote programs and advertise CaRMS events. Given the environmental and financial benefits, CaRMS interviews will likely remain virtual, which highlights the ongoing need for residency programs to use social media platforms to share information with 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".