Increasing tuition fees in a country with two different models of medical education
Bibliographic record
Abstract
CONTEXT Since the early 1990s, medical school tuition fees have increased substantially in all regions of Canada except Quebec. This provides a natural opportunity to examine the effect of tuition fee increases on medical student demographics, indebtedness and financial stress. METHODS We conducted a national survey of medical students in 2007. We compared results for Quebec students with results for students studying in other parts of the country. We also compared results for 2007 medical students with results for medical students who had completed a similar survey in 2001 and with data for the general population. For the 2007 cohort, we also identified predictors of anticipated debt at the time of medical school graduation. RESULTS A total of 7795 students responded to either the 2001 (n = 3871) or 2007 (n = 3924) survey. Median anticipated debt increased from $40 000 in 2001 to $71 000 in 2007 (Canadian dollars). Medical students in Quebec were more likely to have grown up in a lower-income neighbourhood (odds ratio [OR] = 1.22, 95% confidence interval [CI] 1.03-1.44), were less likely to report significant financial stress (OR = 0.43, 95% CI 0.37-0.50) and reported a lower median anticipated debt than medical students in the rest of Canada ($30 000 versus $90 000; p < 0.001). Across Canada, factors associated with increased debt at the time of graduation were higher tuition fees, lower parental income, non-Chinese ethnicity, higher debt at entry to medical school, smaller non-repayable financial grants, longer pre-medical education and higher non-tuition expenses. CONCLUSIONS Quebec medical students differ from their counterparts in the rest of Canada in several notable ways. In particular, medical student debt has increased more and is greater in the rest of Canada than in Quebec. Our findings have implications for doctor human resources planning in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".