How Well Are Radiology Residents Prepared for Practice After Training? A Survey of French-Speaking Quebec Recent Graduates and Department Chiefs
Bibliographic record
Abstract
Objective: Radiology residents must fulfill a standardized curriculum to complete residency and pass a certification exam before they are granted a licence to practice. We sought to evaluate how well residency prepares trainees for practice as perceived by recent graduates and their department chiefs. Subjects and Methods: Radiologists who graduated from the 4 Quebec radiology residency programs between 2005 and 2016 (n = 237) and Quebec radiology department chiefs (n = 98) were anonymously surveyed. Two electronic surveys were created, for recent graduates (74 questions) and for department chiefs (11 questions), with multiple-choice questions and open questions covering all fields of radiology. Surveys were administered between April and June 2016 using the Association des radiologistes du Québec database. Results: Response rate was 75 (31.6%) of 237 from recent graduates and 96% rated their training as excellent or good. Satisfaction with training in computed tomography and magnetic resonance imaging was high, with musculoskeletal (MSK) imaging, particularly MSK ultrasound (US), as well as pediatric, cardiac, and vascular imaging needing more training. Thirty-nine (39.8%) of 98 department chiefs answered the survey and highlighted weaknesses in the interpretation of conventional radiography, obstetrical US, and invasive procedures, as well as limited leadership and administrative skills. Recent graduates and department chiefs both reported difficulties in the ability to interpret daily volume of examinations as scheduled and invasive procedure competency. Conclusion: This survey highlights areas of the radiology curriculum which may benefit from more emphasis during training. Adjustments in the residency program would ensure graduates are successful both in their certification exams and clinical practice.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".