Ethical Issues in Residency Education Related to the COVID-19 Pandemic: A Narrative Inquiry Study
Bibliographic record
Abstract
Abstract Background Amidst the pandemic, residency programs were faced with new challenges to provide care and educate junior doctors (resident physicians). We sought to understand both the positive and negative experiences of first-year residents during COVID-19, as well as to describe potential ethical issues from their stories. Method We used narrative inquiry (NI) methodology and applied a semi-structured interview guide that included questions pertaining to ethical principles as well as 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. A composite story with threads was constructed. Discrepancies were resolved through discussion to attain consensus. Results Eleven residents participated from Internal Medicine (n=2), Family Medicine (n=2), Ophthalmology (n=1), General Surgery (n=1), Pediatrics (n=1), Diagnostic Radiology (n=1), Public Health (n=1), Psychiatry (n=1), Emergency Medicine (n=1). Resident stories had three common themes in which ethical issues were described: 1) Intersecting healthcare and medical education systems , 2) Public health and the public good , 3) Health systems planning/healthcare delivery . Discussion The pandemic exacerbated the lack of autonomy experienced by resident physicians. The notion of public health and the public good at times eclipsed individual wellbeing for residents and patients alike. Conclusion Efforts to understand how resident physicians can be engaged in their own education as well as how they can navigate public health crises with respect to ethical principles could benefit both residency education and healthcare delivery.
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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 | 0.021 | 0.037 |
| 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 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| 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".