Designing COVID-19 vaccine mandates in colleges and universities: a roadmap to the 10 key questions
Bibliographic record
Abstract
COVID-19 transmission among students, faculty, and staff at US institutions of higher education (IHEs) is a pressing concern, especially with the dominance of the highly contagious Delta variant and emergence of the Omicron variant. From the start of the pandemic to May 26, 2021, >700,000 cases were linked to US colleges and universities. To protect their populations and surrounding communities, IHE administrators are increasingly considering COVID-19 vaccine requirements. Roughly one-quarter of the nearly 4,000 college and university campuses across the US have announced COVID-19 vaccine mandates for students or employees. However, deciding to require vaccination is only the first of multiple decisions, as IHEs face complex issues of how to design and refine their mandates, including whether to require boosters. Mandates vary significantly in stringency, implementation, impact on members of the college or university community, and net benefit to the institution. This essay examines 10 key questions that an IHE must face in designing or refining a COVID-19 vaccination mandate. Showing that these 10 questions were carefully considered may be crucial if the institution's mandate is challenged. Ultimately, how an IHE designs its mandate may make the difference between meaningful risk mitigation that advances institutional goals and benefits students, faculty, and staff versus a public health failure that erodes trust, raises equity concerns, threatens to undermine preexisting vaccination requirements, and divides the campus.
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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 | 0.070 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".