A qualitative study of physician perceptions and experiences of caring for critically ill patients in the context of resource strain during the first wave of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led to global shortages in the resources required to care for critically ill patients and to protect frontline healthcare providers. This study investigated physicians' perceptions and experiences of caring for critically ill patients in the context of actual or anticipated resource strain during the COVID-19 pandemic, and explored implications for the healthcare workforce and the delivery of patient care. METHODS: We recruited a diverse sample of critical care physicians from 13 Canadian Universities with adult critical care training programs. We conducted semi-structured telephone interviews between March 25-June 25, 2020 and used qualitative thematic analysis to derive primary themes and subthemes. RESULTS: Fifteen participants (eight female, seven male; median age = 40) from 14 different intensive care units described three overarching themes related to physicians' perceptions and experiences of caring for critically ill patients during the pandemic: 1) Conditions contributing to resource strain (e.g., continuously evolving pandemic conditions); 2) Implications of resource strain on critical care physicians personally (e.g., safety concerns) and professionally (e.g. practice change); and 3) Enablers of resource sufficiency (e.g., adequate human resources). CONCLUSIONS: The COVID-19 pandemic has required health systems and healthcare providers to continuously adapt to rapidly evolving circumstances. Participants' uncertainty about whether their unit's planning and resources would be sufficient to ensure the delivery of high quality patient care throughout the pandemic, coupled with fear and anxiety over personal and familial transmission, indicate the need for a unified systemic pandemic response plan for future infectious disease outbreaks.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".