How comprehensive are nuclear medicine residency websites?
Bibliographic record
Abstract
Our goal for this study was to evaluate the comprehensiveness of nuclear medicine (NM) residency websites from the USA and Canada. The authors searched all the existing NM residency programs as listed in the Fellowship and Residency Electronic Interactive Database and the Canadian Residency Matching Service. We analyzed each website for the presence or absence of 44 elements previously identified as important considerations for medical students applying to residency. We compared criteria prevalence between regions and program size using t-tests and analysis of variance. Our results showed that, of 47 NM residencies, 9 did not have a dedicated website, leaving a total of 38 websites available for evaluation. The individual websites in the USA had a mean of 15 of 44 elements sought; in contrast, Canadian programs had 26 of 44 elements sought. The most common elements included contact e-mail, mailing address, and comprehensive faculty listings. Information about resident hometown, academic interests, and extracurricular interests was only included in 3% of the websites. Only 3% of websites included case description and 11% included rotation schedule. Courses attended were included in 5%, educational resources in 8%, and resident education was included in 5% of the websites. In conclusion, about one in five NM residency programs do not have a publicly available website. The websites that do exist are incomprehensive, containing an average of only 32% of elements sought for the USA programs and 41% of elements sought in Canadian programs. Residency program websites are an important tool in recruiting medical students. Addressing the lack of available websites as well as the gap in content of the websites that does exist may improve recruitment of students to NM residency programs.
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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".