COVID-19: Falling Apart and Bouncing Back. A Collective Autoethnography Focused on Bioethics Education
Bibliographic record
Abstract
The COVID-19 pandemic disrupted academic life worldwide for students as well as educators. The purpose of this study is to shed light on the collective adversity experienced by international medical students and bioethics educators caused by the COVID-19 pandemic in relation to both personal and academic life. The authors wrote their subjective memoirs and then analyzed them using a collective autoethnography method in order to find the similarities and differences between their experiences. The results reveal some consistent patterns in experience that are captured in two metaphors:Falling apartandBouncing back. “Falling apart” involves the breakdown of daily lives during the initial stages of the pandemic, shown through subjective quotes contextualized through the authors’ commentary. The consensus is that returning home and the transition to remote education were the two main reasons for the breakdown. “Bouncing back” encompasses the authors’ recovery after the initial breakdown, achieved by acquiring new information about the virus, discovering how to continue their hobbies at home, such as working out or dancing, and learning to adjust exam expectations. At the educational level, the bioethics course, which guided students through the ethical dilemmas of the pandemic, played an important role in the recovery/bouncing back process. For that reason, we report on how it was to learn about and teach this subject during the pandemic, and how bioethics knowledge was applied for better understanding and coping with some of the moral dilemmas related to the pandemic. The study testifies to the importance of bioethics education during a pandemic and explains how this can contribute to shaping the moral resilience of future medical practitioners.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".