Empower Your Applicants: Why Residency Programs Need to up Their Website Game
Bibliographic record
Abstract
We want it to be easy.We use the internet on a daily basis to search for information.We rejoice when the first website gives us clear access to what we seek; we become increasingly frustrated if we go in circles from one site to another without finding what we need.Imagine what it must be like for a final-year medical student trying to research residency programs.The format and the information that program websites contain is highly variable and often out of date.Program descriptions on the CaRMS website contain detailed text but are not easily searchable.Many students remain unaware of additional resources found on the CanPrePP website.It must be a very timeconsuming process indeed!It has long been acknowledged that applying to residency is an extremely stressful process.1,2 Statistics about the number of available positions or student satisfaction with match results do not account for the time and effort that went into making choices and setting rank lists.The CaRMS match moved to virtual interviews in 2021 and visiting electives only resumed to a very limited extent in the fall of 2022, with full capacity not expected until the fall of 2023. 3 These changes resulting from the COVID-19 pandemic made it even harder for applicants to learn about programs and make choices; they increasingly rely more on virtual resources to gather information.4 Residency program websites can serve as an important source of information for students 5 and have been shown to influence applicants rank lists.6 However, the study by Tsai and colleagues in this issue of the Journal 7 confirms that wide variation exists in how programs use websites to showcase their strengths, curriculum, and resources.At a broad level, US program websites were found to be more comprehensive than Canadian websites.Some of this can be explained by the fact that Canadian resident salaries, vacation policies, and insurance plans are province-dependent, while benefits in the USA can be department-and institution-specific. Canadian programs may expect applicants to look elsewhere for this information as well as information on selection criteria, application process, and application dates.Tsai and colleagues adapted their assessment tool from prior studies.Although it includes a broad range of criteria, most items are "all or none," either present or absent on a given website.For example, the "neighbourhood" criterion was fulfilled if the website included at least one detail of the surrounding neighborhood, while
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.122 | 0.037 |
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".