Overcoming Financial and Social Barriers during COVID-19: A Medical Student-led Medical Education Innovation
Bibliographic record
Abstract
Background: Underrepresented minorities in medicine (URMM) may face financial and social limitations when applying to medical schools. The computer-based assessment for sampling personal characteristics (CASPER) test is used by many medical schools to assess the nonacademic competencies of applicants. Performance on CASPER can be enhanced by coaching and mentorship, which URMMs often lack, for affordability reasons, when applying to medical schools. Methods: The CASPER Preparation Program (CPP) is a free, online, 4-week program to help URMM prepare for the CASPER test. CPP features free medical ethics resources, homework and practice tests, and feedback from tutors. Two of CPPs major objectives include relieving URMM of financial burdens and increasing their accessibility to mentorship during the COVID-19 pandemic. A program evaluation was conducted using anonymous, voluntary postprogram questionnaires to assess CPPs efficacy in achieving the aforementioned objectives. Results: Sixty URMMs completed the survey. The majority of the respondents strongly agreed or agreed that CPP relieves students of financial burden (97%), is beneficial for applicants with low-socioeconomic statuses (98%), provides students with resources they could not afford (n = 55; 92%), and enables access to mentors during the pandemic (90%). Discussion: Pathway coaching programs, such as the CASPER Preparation Program, have the potential to offer URMMs mentorship and financial relief, and increase their confidence and familiarity with standardized admission tests to help them matriculate into medical schools.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".