Factors Influencing Radiology Residents' Fellowship Training and Practice Preferences in Canada
Bibliographic record
Abstract
PURPOSE: The study aimed to examine the postresidency plans of Canadian radiology residents and factors influencing their fellowship choices and practice preferences, including interest in teaching and research. METHODS: Institutional ethics approval was obtained at McMaster University. Electronic surveys were sent to second to fifth-year residents at all 16 radiology residency programs across Canada. Each survey assessed factors influencing fellowship choices and practice preferences. RESULTS: A total of 103 (31%) Canadian radiology residents responded to the online survey. Over 89% from English-speaking programs intended to pursue fellowship training compared to 55% of residents from French-speaking programs. The most important factors influencing residents' decision to pursue fellowship training were enhanced employability (46%) and personal interest (47%). Top fellowship choices were musculoskeletal imaging (19%), body imaging (17%), vascular or interventional (14%), neuroradiology (8%), and women's imaging (7%). Respondents received the majority of their fellowship information from peers (68%), staff radiologists (61%), and university websites (58%). Approximately 59% planned on practicing at academic institutions and stated that lifestyle (43%), job prospects (29%), and teaching opportunities (27%) were the most important factors influencing their decisions. A total of 89% were interested in teaching but only 46% were interested in incorporating research into their future practice. CONCLUSIONS: The majority of radiology residents plan on pursuing fellowship training and often receive their fellowship information from informal sources such as peers and staff radiologists. Fellowship directors can incorporate recruitment strategies such as mentorship programs and improving program websites. There is a need to increase resident participation in research to advance the future of radiology.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".