Subspecialty Employment Needs in Academic Radiology Settings Across Canada
Bibliographic record
Abstract
PURPOSE: The purpose of this survey was to identify current and projected subspecialty employment needs across Canadian academic radiology practices. METHODS: An electronic survey was distributed to academic radiology department heads within the faculties of medicine at Canadian universities between September and October 2019. Respondents identified the number of partnership track radiologists hired in the last academic year, the number of fellowship-trained new hires, and the top 3 subspecialties for new and prospective hires. Descriptive statistics were used to summarize the data. RESULTS: Nine academic radiology department heads responded to the survey (75% response rate) with good regional representation across Canada. Ninety-five percent of new hires within the last academic year were subspecialty fellowship trained. The top subspecialties for new hires in the last year were abdominal imaging and interventional neuroradiology, with 77.8% and 44.4% of academic leaders reporting them as one of the top 3 subspecialties, respectively. The top 3 subspecialties for prospective hires in the next academic year included musculoskeletal imaging (n = 6, 66.7%), followed by abdominal imaging (n = 5, 55.6%), with pediatric radiology (n = 3, 33.3%) and cardiothoracic imaging (n = 3, 33.3%) tying for third place. There was some variability in the subspecialty needs for hires between regions. CONCLUSIONS: The survey results provide valuable information about the current and future subspecialty needs of academic radiology practices. The data obtained can provide guidance to trainees regarding fellowship training options that will optimize their future employability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".