Increasing Fellow Recruitment: How Can Fellowship Program Websites Be Optimized?
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the importance of factors considered by orthopaedic trauma fellowship applicants when evaluating programs. We hypothesized that applicants will prioritize consistent factors when applying to programs. In addition, we assessed how the applicants use the Internet to research potential fellowships. Our goal is to provide fellowships with information to optimize both their fellowship and online contents. METHODS: At the 2018 and 2019 Orthopaedic Trauma Association meetings, a paper survey was given to each attendee of multiple fellowships' informational sessions. The survey consisted of 25 factors that applicants may consider when evaluating fellowships ranked on a 1-to-5 Likert scale. Additional questions were asked to determine how applicants use the Internet and social media when researching fellowships. RESULTS: We received 111 surveys (roughly a 56% response rate). Ninety-eight applicants (88%) indicated that they use fellowship websites to research fellowships. The utilization of fellowship websites was markedly greater than the use of other online resources. The highest rated factors surveyed were surgical experience (mean 4.95; SD 0.26), pelvic and acetabular experience (4.80; 0.52), lower extremity fracture experience (4.75; 0.58), and current faculty at the fellowship (4.55; 0.78). The lowest rated factors were the ability to moonlight (2.04; 1.08), salary (1.88; 1.12), and spine trauma experience (1.45; 0.87). Surgical experience and pelvic/acetabular experience were rated markedly higher than every other factor surveyed. DISCUSSION: To our knowledge, this is the first study to demonstrate that most orthopaedic trauma fellowship applicants use fellowship websites when researching programs. Programs may use this study to optimize their fellowship experience to reflect what the applicants value. In addition, programs may use this study as a guide when updating their websites. Fellowships with informative websites that meaningfully highlight their fellowship experience may have a competitive edge in attracting applicants to their programs.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".