Supporting Student Wellness to Enable Resiliency During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has created a unique time, requiring flexibility to adapt to evolving circumstances. The shift from being fully immersed as a doctoral student on campus, to being a full-time mother and online student at home and largely isolated, was a significant and challenging change. Paradoxically, I was filled with gratitude for additional immediate family time while I also felt incredible stress due to a lack of dedicated professional time. Determined to persevere, I embraced three strategies that fostered my resilience during the COVID-19 pandemic: (1) listening to course content; (2) dedicating time to daily physical activity; and (3) spending time outdoors. Moving forward, I will continue to prioritize my wellness by embracing the strategies identified here, and I encourage universities to explore how student wellness can be more comprehensively and proactively supported.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".