Anticipated debt and financial stress in medical students
Bibliographic record
Abstract
BACKGROUND: While medical student debt is increasing, the effect of debt on student well-being and performance remains unclear. AIM: As a part of a larger study examining medical student views of their future profession, data were collected to examine the role that current and anticipated debt has in predicting stress among medical students. METHOD: A survey was administered to medical students in all four years at the University of Toronto. Of the 804 potential respondents across the four years of training, 549 surveys had sufficient data for inclusion in this analysis, for a response rate of 68%. Through multiple regression analysis, we evaluated the correlation between current and anticipated debt and financial stress. RESULTS: Although perceived financial stress correlates with both current and anticipated debt levels, anticipated debt was able to account for an additional 11.5% of variance in reported stress when compared to current debt levels alone. CONCLUSIONS: This study demonstrates a relationship between perceived financial stress and debt levels, and suggests that anticipated debt levels might be a more robust metric to capture financial burden, as it standardizes for year of training and captures future financial liabilities (future tuition and other future expenses).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".