What is the financial state of medical students from rural backgrounds during tuition fee deregulation?
Bibliographic record
Abstract
INTRODUCTION: We sought to examine the financial state of medical students from rural backgrounds during a time of tuition fee deregulation. METHODS: We surveyed incoming classes from 2007 to 2011 at the University of Calgary. Community background, expected educational debt at graduation, educational debt at entry to medical school and parental income were collected for analysis. Data were analyzed using the Χ² test, analysis of variance and the Newman-Keuls multiple comparison test. RESULTS: The overall response rate was 95.3%. Of the 571 (93.5%) respondents who supplied data on their background and debt, 94.4% expected to have educational debt at graduation. The mean projected educational debt at graduation by medical students from both rural ($107 226 [95% confidence interval (CI) $98 030-$116 423]) and regional ($99 456 [95% CI $91 905-$107 006]) backgrounds was significantly greater than the debt projected by students from metropolitan ($88 565 [95% CI $83 607-$93 524]) backgrounds. Medical students who came from rural backgrounds had the highest mean debt at entry to medical school ($33 053 [95% CI $25 715-$40 391]) compared with their peers from regional ($23 253 [95% CI $16 621-$29 885]) and metropolitan ($22 053 [95% CI $17 344-$26 762]) backgrounds. Students of rural origin also had parents whose mean income ($104 024 [95% CI $75 976-$132 173]) was significantly lower than the mean parental income of their peers who originated from regional ($143 167 [95% CI $119 898-$166 435]) and metropolitan ($150 339 [95% CI $135 241-165 438]) centres. CONCLUSION: Rising tuition and subsequent debt may be affecting the diversity of medical students' backgrounds. Financial programs dedicated to rural-background students and their interest in medicine may become necessary.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".