Investigating Canadian Radiology Residents’ Personal Financial Literacy: A Nation-Wide Assessment
Bibliographic record
Abstract
Purpose: Canadian resident physicians carry large debt to finance their education, which impacts their wellness and their future decision making. The objective of this observational study is to assess the financial literacy of Canadian radiology residents through testing their financial knowledge and examining their current financial status. Methods: A survey was designed to assess the financial literacy and current financial status of radiology residents, which was distributed to Canadian radiology residents via Google Forms. Descriptive analyses on preliminary data and the association between level of training and financial quiz scores were obtained. Results: 104 valid responses from 16 universities were received. The majority (53%) of residents indicated that their debt was greater than $150 000. Residents on average scored 71% on the financial quiz and the scores were not associated with training level ( P = .71). The majority (89%) of residents indicated a strong interest in a formal financial literacy curriculum, with 80% preferring a physician-led curriculum. Conclusion: Overall, residents face a high debt burden. Current resident physicians value a formal financial literacy curriculum as a part of their residency program despite existing financial knowledge. Most importantly, residents feel that a curriculum created with involvement of other physicians would be optimal.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".