Anchor universities as leaders in the well-being movement: lessons learned from the University of California Healthy Campus Network & pandemic
Bibliographic record
Abstract
The University of California (UC) Healthy Campus Network (HCN) is a robust network of diverse coalitions across 10 UC campuses, 5 UC teaching hospitals, and UC Agriculture & Natural Resources working to promote individual campus and systemwide changes toward a culture of health and equity. The success of this work has been evident in the HCN's ability to quickly pivot to meet emergent needs during the COVID-19 pandemic, including social support through the UC Diabetes Prevention Program, tap water access for essential workers through the UC Healthy Beverage Initiative, and food security efforts through the UC Global Food Initiative. Building a culture of health and equity across a large public university system generated valuable lessons learned which enhanced the UC's preparedness and resilience in the face of the pandemic, and other institutions may benefit from these best practices to respond effectively to emergencies and thrive in states of relative normalcy.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".