Making Room at the Table: Expanding the MCAT Fee Assistance Program in Canada via Student and Trainee Representation
Bibliographic record
Abstract
To the Editor: The Association of Faculties of Medicine of Canada (AFMC) Future of Medical Education in Canada report in 2010 sets out diversity goals for medical school admissions. 1 One such goal was to increase representation of students from low socioeconomic status backgrounds. To advance this goal, the AFMC collaborated with the Association of American Medical Colleges to pilot a Medical College Admission Test (MCAT) fee assistance program (FAP) for Canadian applicants beginning in 2018. Unfortunately, this initiative was underused due to its inaccessibility (e.g., lack of effective student outreach, few instructions on how to apply). To address this issue, the AFMC partnered with 2 student/trainee-led groups, Price of a Dream (POD) and Community of Support (COS), on a quality improvement project to increase applicant utilization of the MCAT FAP during the 2020–2021 application cycle. The results were astounding. Within a year, there was a 39% increase in applicants to the MCAT FAP and a 46% increase in the number of awardees. What changed? The inclusion of students and trainees on the outreach committee. POD members include trainees who, as former medical school applicants, understand the impact of financial barriers. COS members include premedical students who have successfully applied to the MCAT FAP. POD and COS leveraged their proximity to applicants and understanding of barriers to develop and implement an applicant engagement plan focused on increasing MCAT FAP accessibility. This plan included (1) hosting informational webinars, (2) developing a toolkit to walk applicants through the application, and (3) increasing outreach via social media. The AFMC has been open to students’ questions, but having access to students and trainees made some applicants more comfortable seeking help with their applications. This project also provided role-modeling, helping students from low socioeconomic status backgrounds meet peers in medicine. The expression “nothing about us without us” (a rough translation of nihil novi nisi commune consensu) was first documented in 1505, 2 and it still holds true today. As equity, diversity, and inclusion efforts such as the MCAT FAP continue to gain traction, reflect on the leadership tables at your institutions: Have you engaged students and trainees? If not, it is time to make room.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".