Development, implementation, and uptake of a novel CaRMS residency recruitment committee strategy in the era of COVID-19
Bibliographic record
Abstract
INTRODUCTION: Given restrictions on electives outside of medical students' home institutions during the COVID-19 pandemic, the objective of this study was to create a novel recruitment strategy for the University of Ottawa's (uOttawa) urology residency program. METHODS: A steering committee was formed and created a three-part recruitment strategy that included a new uOttawa urology website, a residency program social media campaign (Twitter and Instagram), and a virtual open house (VOH). Descriptive data from the website and Instagram and Twitter accounts were collected. Attendees of the VOH completed a mixed-methods survey, which collected quantitative and qualitive responses assessing aspects of the VOH and virtual resource use. RESULTS: From August 1 to December 31, 2020, the uOttawa urology website had 1707 visits. The Twitter account had a total of 29 000 views with 1000-5000 views per tweet. Thirty-one candidates attended the VOH. Survey responders reported that the most frequently used resources to gain knowledge of the program were the website (81%) and Twitter account (71%). The most helpful and informative resources were the uOttawa urology website, the VOH, and direct conversations with residents arranged through the website. Despite not having completed an elective, 26 students (84%) felt they had an understanding of what it might feel like to train in the program. Suggestions by students for future initiatives included one-on-one virtual meetings, another VOH, and more information on selection processes. CONCLUSIONS: A multifaceted, virtual recruitment strategy can be implemented to improve candidate understanding and engagement with residency programs while visiting elective opportunities remain limited.
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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 | 0.113 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".