Ethical issues in residency education related to the COVID-19 pandemic: a narrative inquiry study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic introduced new challenges to provide care and educate junior doctors (resident physicians). We sought to understand the positive and negative experiences of first-year resident physicians and describe potential ethical issues from their stories. METHOD: We used narrative inquiry (NI) methodology and applied a semistructured interview guide with questions pertaining to ethical principles and both positive and negative aspects of the pandemic. Sampling was purposive. Interviews were audio recorded and transcribed. Three members of the research team coded transcripts in duplicate to elicit themes. Discrepancies were resolved through discussion to attain consensus. A composite story with threads was constructed. RESULTS: 11 residents participated across several programmes. Three main themes emerged from the participants' stories: (1) complexities in navigating intersecting healthcare and medical education systems, (2) balancing public health and the public good versus the individual and (3) fair health systems planning/healthcare delivery. Within these themes, participants' journeys through the first wave were elicited through the threads of (1) engage us, (2) because we see the need for the duty to treat and (3) we are all in this together. DISCUSSION: Cases of the ethical issues that took place during the COVID-19 pandemic may serve as a foundation on which ethics teaching and future pandemic planning can take place. Principles of clinical ethics and their limitations, when applied to public health issues, could help in contrasting clinical ethics with public health ethics. CONCLUSION: Efforts to understand how resident physicians can navigate public health emergencies along with the ethical issues that arise could benefit both residency education and healthcare systems.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".