The Vot-ER Healthy Democracy Campaign: A National Medical Student Competition to Increase Voting Access
Bibliographic record
Abstract
Abstract Problem Voting affords citizens a direct say in the leaders and policies that affect their health. However, less than 20% of eligible U.S. citizens have been offered the chance to register to vote at a government-funded agency like a hospital or clinic that provides Medicaid or Medicare services. Medical students are well positioned to increase voting access due to their interactions with multiple actors in health care settings, including patients, visitors, colleagues, and others. Approach Vot-ER, a nonpartisan, nonprofit organization that aims to promote civic engagement in health care settings, launched the inaugural Healthy Democracy Campaign from July 20 to October 9, 2020. As part of this national, gamification-based competition, medical student captains were recruited to lead teams of health care trainees and professionals that helped eligible adults start the voter registration and/or mail-in ballot request process before the November 2020 elections. Post competition, medical student captains were surveyed about their motivations for participating and skills and knowledge gained. Outcomes In total, 128 medical student captains at 80 medical schools in 31 states and the District of Columbia formed teams that helped 15,692 adults start the voter registration and/or mail-in ballot request process. Eighty-two (64.1%) captains responded to the post competition survey, representing 56 (70.0%) of the participating schools. The top-ranked motivation for participating in the campaign was the desire to address social and racial inequities (37, 45.1%). Respondents reported gaining skills and knowledge in several aspects of civic engagement, including community organizing (67, 81.7%) and voting rights (63, 76.8%). The majority of respondents planned to incorporate voter registration into their future practice (76, 92.7%). Next Steps Future Healthy Democracy Campaigns will aim to continue closing the voting access gap and promote the long-term inclusion of hands-on civic engagement in medical education and practice.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".