National Survey to Identify Subspecialties at Risk for Physician Shortages in Canadian Academic Radiology Departments
Bibliographic record
Abstract
PURPOSE: To identify subspecialty fields in Canadian academic radiology departments that are at risk for future manpower shortages. To determine reasons for the potential shortages and suggest potential solutions. METHODS: An anonymous online survey was sent by e-mail to radiology residents and academic radiology department heads in Canada. The survey was open from April 1 to August 1, 2006. Statistical analysis by using the SAS Frequency Procedure was performed on the results. RESULTS: Interventional radiology, neuroradiology, mammography, cardiac imaging, and pediatric radiology were identified as areas in which there will be increasing workforce demands. Mammography, pediatric radiology, and cardiac imaging were identified as areas in which there will be a potential decrease in supply. Of the residents, 65.83% intended on pursuing subspecialty training. Priorities were interesting work, job availability, and work schedule. Nuclear medicine, mammography, pediatric radiology, and interventional radiology were identified as the top 4 areas in which residents specifically did not want to pursue further subspecialty training. Only 15% of resident respondents received career counseling during residency, and only 50% of those residents thought it was adequate. CONCLUSIONS: Our survey results indicate that mammography, cardiac imaging, and pediatric radiology are at risk for manpower shortages, and interventional radiology may be at risk. Increased efforts to recruit trainees may be necessary to ensure that these subspecialties maintain their presence in the future. Only 15% of the surveyed residents received career counseling during residency. This is a relatively untapped forum that academic staff could use to help recruit new trainees into these underserved subspecialties.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".