Exploring the Impacts of the Beginning of the COVID-19 Pandemic on Critical Care Physicians and the Delivery of Patient Care in Eight Countries: A Qualitative Interview-Based Study
Bibliographic record
Abstract
Purpose: To understand critical care physician experiences across multiple countries with the COVID-19 pandemic to inform future pandemic preparedness planning. Methods: In this qualitative descriptive study, 16 critical care physicians (from eight countries) identified in convenience and purposive sampling took part in individual semi-structured interviews from April 7, 2020 to August 27, 2020 that captured the first wave of the pandemic. Open coding was conducted by two researchers who facilitated inductive thematic analysis. Results: Key themes identified following thematic analysis included the following: (a) sourcing and implementation of trusted information; (b) health systems–level preparedness with accessible supports; (c) institutional adaptations, including changes to patient care; (d) professional safety and occupational well-being; (e) triage and restricted visitation policies; and (f) managing personal familial responsibilities. Conclusion: The COVID-19 pandemic transformed the ways in which critical care physicians cared for their patients and personally coped with challenges. Perspectives of critical care physicians are important for ongoing pandemic planning and should be included in future pandemic policy development.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".