American Anesthesiology Residency Programs: Website Usability Analysis
Bibliographic record
Abstract
BACKGROUND: The Association of American Medical Colleges has recently issued recommendations for the upcoming 2022-2023 application cycle that residency programs should conduct all interviews for this upcoming application cycle over the web. In light of these recommendations, many students will have limited exposure to anesthesiology programs and will rely on information gleaned digitally. This change means that the aspects of program websites used to provide information, such as size, structure, location, requirements, and contact information, will be crucial in helping prospective residents decide where and how to apply in the future. An evaluation of website usability, which includes initial appearance along with factors that influence its ease of navigation and convenience of use, can thus be applied to anesthesiology residency websites. Areas of need can be targeted to increase web presence and provide effective pathways to exhibit the different attributes of their programs to future applicants. OBJECTIVE: This study aimed to compile a list of US anesthesiology residency programs and their websites while objectively analyzing the websites using a formally published usability scoring system, as well as to identify positive and negative trends to offer areas of improvement among anesthesiology residency websites. METHODS: We included only 114 US anesthesiology residency program websites in our sample set, since some websites we analyzed showed errors or inconclusive. Website usability was separated into 4 distinct categories for analysis based on methodology outlined in previous literature on both health care website usability and residency website usability. The 4 categories were Accessibility, Marketing, Content Quality, and Technology. Each website was then analyzed and scored based on key components highlighted within the 4 categories. The multiple factors were then graded using a percentage system to create a comprehensive score for each program. RESULTS: The highest scoring category was Content Quality (mean 4.7, SD 2.48, SE 0.23). The lowest scoring category was Technology (mean 0.9, SD 0.38, SE 0.04). CONCLUSIONS: Through the application of a health care website usability framework, multiple anesthesiology residency programs were analyzed and scored in the areas of Accessibility, Marketing, Content Quality, and Technology, which allowed us to determine the effectiveness of the usability of these websites to convey information to their end user. Websites must communicate vital information, with usability at the forefront, to continue to grow, especially as the United States faces challenges due to the COVID-19 pandemic. Our recommendation is that anesthesiology programs should strive to improve website usability to increase the ease by which applicants can collect vital information about anesthesiology programs. A few proposed solutions include making changes such as decreasing error pages on websites, migrating away from using in-line cascading style sheets, and improving web page loading speeds to improve the Technology category.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".