Improving Financial Literacy Using the Medical Mini-MBA at a Canadian Medical School
Bibliographic record
Abstract
Introduction Financial literacy correlates with less debt and better retirement planning. Medical students, residents, and physicians often have poor financial literacy and large amounts of debt. We measured baseline financial literacy and whether it improved with the administration of a financial literacy course. Methods We created the Medical Mini-MBA,a six-week financial literacy course that targeted gaps in financial literacy among medical students and residents. Weekly topics included personal finance, investing, real estate and mortgage, physician billing and payment models, income and tax, and choosing a medical specialty. A 46-question financial literacy assessment was delivered to participants before and after the course. Results Of the 276 who participated in the course, 179 (64.86%) participated in the study. Participants who completed the course improved their financial literacy score by 10.10/46.00±5.12 (n=93, p<0.001). Self-assessment of financial literacy was positively correlated with financial literacy exam scores (r=0.366, p<0.001). Demographics such as gender, geography, education level, and first-degree relatives who are/were physicians had no effect on financial literacy scores. Conclusions The Medical Mini-MBA improved financial literacy at a Canadian medical school. Implementation of the coursemay equip medical students and residents for financial decisions. It avoids financial conflicts of interest and can supplement the medical curriculum.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".