Riding the Coronacoaster: Learning, Teaching, and Living at a Health Sciences Campus during the COVID-19 Pandemic
Bibliographic record
Abstract
This collaborative autoethnography examines how we (four students and a professor of community-engaged research) used our experiences to make sense of life during COVID‑19. Engaging in a collective approach to autoethnographic writing, we highlight different perspectives of a cultural moment shaped by the science denialism and untruths that define US governmental practices and approaches to the pandemic. We share how we are dealing with COVID‑19 discourses that run counter to the scientific foregrounding of our STEM (Science, Technology, Engineering, and Math) and health and medical sciences training, reflect on the role of the pandemic in shifting post-baccalaureate plans, navigate the lockdown while participating in racial justice protests in Minneapolis, and examine the experiences of being essential healthcare workers while in school. By situating these shifting ways of learning, teaching, and engaging as portents of the difficulties we may continue to face, we show the possibilities of narrative methods in imagining and implementing post-pandemic healthcare practices grounded on a praxis of community justice and collective care.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.035 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".