Investigating Canadian Radiology Residents’ Personal Financial Literacy: A Nation-Wide Assessment.
Bibliographic record
Abstract
Abstract Introduction:Resident physicians throughout Canada carry large debt to finance their education. The literature suggests that debt and money management both play a large role in day-to-day life decisions and in deciding the future of physicians. The objective of this observational study is to assess the financial literacy of Canadian radiology residents through testing their financial knowledge and obtaining an understanding of their financial wellness. Methods:A survey was designed to assess the financial literacy and current financial status of radiology residents. After a pilot run to ensure robustness, the survey was distributed nationally 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:In total, 104 valid responses from 16 universities were received. Respondents’ levels of training were spread across the 5 training levels. The majority (53%) of residents indicated that their debt was greater than $150,000, yet only 12% of residents stated they had a formal financial residency curriculum. The average score of residents on the financial literacy quiz was 71% and survey quiz scores were not associated with training level (p = 0.71). The majority (89%) of residents indicated a strong interest in a formal financial literacy curriculum, with 80% stating they would like the curriculum delivered by other physicians. Conclusion:Our findings demonstrate unique insight into the financial literacy and demographics of Canadian radiology residents. Overall, residents face a high debt burden and the majority feel that they do not have an adequate financial literacy curriculum in their program. 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".