Website usability analysis of United States emergency medicine residencies
Bibliographic record
Abstract
Objectives: The Council of Residency Directors (CORD) in Emergency Medicine (EM) has recommended that all residency programs should conduct virtual interviews for the 2020 to 2021 application cycle due to the COVID-19 pandemic. While factors such as geographical region, city, program size, or hospital affiliation are not modifiable, EM residencies can bridge the information gap created by a lack of face-to-face interaction by representing themselves digitally. Measuring usability provides an objective method for EM residencies to improve their Web presence and effectively represent themselves to applicants. Methods: Our sample set included 55 U.S. EM residency program websites. Using methodology replicated from previous literature on health care website usability, we divided usability into four categories for quantifiable analysis: accessibility, marketing, content quality, and technology. Analysis was performed on each website and scored in all four categories. A "general usability" score was calculated for each website using a composite of the key factors within the four categories. Using a weighted percentage across all of the factors, an overall score was calculated. Results: Content quality was the overall highest scoring category (mean ± SD = 5.4, SE = 0.33). The overall lowest performing category was technology (mean ± SD = 0.8 ± 0.09, SE = 0.01). Conclusions: Measuring usability can help EM residency programs identify ways to improve their Web presence. To effectively promote their programs, residencies need quality content that communicates their key features. Our recommendation is for all residency programs to periodically perform website audits and apply the usability measures outlined to improve their digital presence, especially during times when face-to-face interactions will be 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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".