Critical Evaluation of the Efficiency of Colorectal Fellowship Websites: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Websites are an important source of information for fellowship applicants, as they can influence ongoing interest and potential program selection. OBJECTIVE: This study aims to evaluate the current state of colorectal fellowship websites. METHODS: This cross-sectional study evaluates the quantity and quality of information available on websites of colorectal fellowship programs verified by the Accreditation Council for Graduate Medical Education in 2019. RESULTS: A total of 63 colorectal fellowships were included for evaluation. Websites were surveyed for content items that previous studies have found to be influential to program applicants. The 58 (91%) programs with a functional website were evaluated using an information index (calculated as a function of availability of content items concerning education, application, personnel, and benefits) and an interactive index (calculated as a function of accessibility and usability of the webpage). Programs had a median total score of 27.8 (IQR 21.5-34.5) of 79. The median score for the interactive index was 7.5 of 15 and for the information index was 20 of 64. The median scores for website application, education, personnel, and benefits or life considerations were 5, 5.5, 3.3, and 4 of 13, 24, 13, and 14, respectively. There was no difference in total score between programs in different geographical regions (P=.46). CONCLUSIONS: Currently, colorectal surgery fellowship program websites do not provide enough content for applicants to make informed decisions. All training programs, regardless of specialty, should evaluate and improve their digital footprint to ensure their websites are accessible and provide the information desired by applicants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".