The Direct Economic and Opportunity Costs of the Medical College Admissions Test (MCAT) for Canadian Medical Students
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background The Association of Faculties of Medicine of Canada, Future of Medical Education in Canada report shared a collective vision to improve social accountability, including a review of admissions policies to enhance student diversity. This study explored if and how the Medical College Admissions Test (MCAT) might mediate the socioeconomic diversity of Canadian medical schools by quantifying the costs and other cost-related factors of preparing for the exam. Methods A 34-question anonymous and bilingual (English and French) online questionnaire was sent to the 2015 first-year cohort of Canadian medical students. Developed collaboratively, the survey content focused on MCAT preparation and completion activities, associated costs, and students' perceptions of MCAT costs. Findings The survey response rate was 32%. First-year medical students were more likely than the Canadian population to belong to high-income families (63% vs. 36%) and less likely to be from rural locations (4.5% vs. 19%). Use of MCAT preparation materials was reported by nearly every MCAT test-taker (95.3%): of those, 76.4% used free practice tests; 59.8% paid for practice tests; 45.1% registered for preparation courses; and 3.3% hired a private tutor. In terms of writing the MCAT, the total economic costs per respondent are estimated at $6,357 ($4,755-$7,958) and total direct costs per respondent are estimated at $2,970 ($1,882- $4,058). Opportunity costs represented the majority of economic costs, at $3,387 ($2,872 - $3,901), or 53.2%. MCAT preparation costs are estimated to be $2,372 ($1,373-$3,372), or 79.9% of total direct costs and 37.3% of economic costs. Most respondents agreed, 76%, that the MCAT posed a financial hardship. Conclusion The financial demands of preparing for and completing the MCAT quantified in this study highlight an admissions requirement that is likely contributing to the current student diversity challenges in Canadian medical schools. In the spirit of social accountability, perhaps it is time to prioritize equitable alternative for assessing applicants' academic readiness for medical school.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".