Effects of rising tuition fees on medical school class composition and financial outlook.
Bibliographic record
Abstract
BACKGROUND: Since 1997, tuition has more than doubled at Ontario medical schools but has remained relatively stable in other Canadian provinces. We sought to determine whether the increasing tuition fees in Ontario affected the demographic characteristics and financial outlook of medical students in that province as compared with those of medical students in the rest of Canada. METHODS: As part of a larger Internet survey of all students at Canadian medical schools outside Quebec, conducted in January and February 2001, we compared the respondents from Ontario schools with those from the other schools (control group). Respondents were asked about their age, sex, self-reported family income (as a direct indicator of socioeconomic status), the first 3 digits of their postal code at graduation from high school (as an indirect indicator of socioeconomic status), and importance of financial considerations in choosing a specialty and location of practice. We used logistic regression models to see if temporal changes (1997 v. 2000) among Ontario medical students differed from those among medical students elsewhere in Canada apart from Quebec. RESULTS: Responses were obtained from 2994 (68.5%) of 4368 medical students. Across the medical schools, there was an increase in self-reported family income between 1997 and 2000 (p = 0.03). In Ontario, the proportion of respondents with a family income of less than $40,000 declined from 22.6% to 15.0%. However, compared with the control respondents, the overall rise in family income among Ontario students was not statistically significant. First-year Ontario students reported higher levels of expected debt at graduation than did graduating students (median $80,000 v. $57,000) (p < 0.001), and the proportion of students expecting to graduate with debt of at least $100,000 more than doubled. Neither of these differences was observed in the control group. First-year Ontario students were also more likely than fourth-year Ontario students to report that their financial situation was "very" or "extremely" stressful and to cite financial considerations as having a major influence on specialty choice or practice location. These differences were not observed in the control group. INTERPRETATION: At Canadian medical schools, there are fewer students from low-income families in general. However, Ontario medical students report a large increase in expected debt on graduation, an increased consideration of finances in deciding what or where to practise, and increasing financial stress, factors that are not observed among students in other provinces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".