An Evaluation of the Content of Canadian and American Nuclear Medicine Fellowship Websites
Bibliographic record
Abstract
Background: Radiology trainees frequently use the Internet to research potential fellowship programs across all subspecialties. For a field like nuclear medicine, which has multiple training pathways, program websites can be an essential resource for potential applicants. This study aimed to analyze the online content of Canadian and American Nuclear Medicine fellowship websites. Materials and Methods: The content of all active Canadian and American Nuclear Medicine fellowship websites was evaluated using 26 criteria in the following subdivisions: application, recruitment, education, research, clinical work, and incentives. Fellowships without websites were excluded from the study. Scores were summed per program and compared by geographic region and ranking. Results: A total of 42 active Canadian and American Nuclear Medicine fellowship programs were identified, of which 39 fellowships had dedicated fellowship websites available for the analysis. On average, fellowship websites contained 34.4% (9 ± 3.3) of the 26 criteria. Programs did not score differently on the criteria by geographical distribution ( P = .08) nor by ranking ( P = .18). Conclusion: Most Canadian and American Nuclear Medicine fellowship websites are lacking content relevant to prospective fellows. Addressing inadequacies in online content may support programs to inform and recruit residents into fellowship 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".