Tales from a Hospital Entrance Screener: An Autoethnography and Exploration of COVID-19, Risk, and Responsibility
Bibliographic record
Abstract
This autoethnography explores my experiences as a hospital entrance screener during the first wave of the pandemic in a hospital in Ontario, Canada. In April 2020, I was redeployed from my research role to a hospital entrance screener. Focused on my lived experiences, the purpose of this research is to provide a glimpse into what it was like to work in a hospital early in the pandemic, to understand these experiences in relation to sociocultural meanings, and to try to make sense of my experiences with COVID-19. Through reflections, I offer a critical account of my experiences working as a screener and analyze personal reflections about my thoughts, feelings, and experiences from a post-structural lens. My analysis reveals several themes: responsibilization, risk, emotional labor, policing and securitization, and the hero discourse. My experiences as a screener demonstrate the complexities of the COVID-19 society and experience.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".