A Qualitative Study of Resource Allocation Decisions Navigated by Frontline Critical Care Providers During The COVID-19 Pandemic: Educational Insights and Implications
Bibliographic record
Abstract
Background: Insufficient resources and dynamic infection control policies during the COVID-19 pandemic created a resource-strained environment which necessitated frontline Health Care Providers (HCPs) to make ethical decisions frequently. Many of these ethical decisions included allocating scarce resources to optimally prioritize patients, resources, and clinician time. The transition from usual patient-centred care to care centred around infection control mandates and rationing resources forced HCPs to balance competing demands while trying to uphold high standards of care. This research aimed to understand the resource allocation decisions HCPs had to navigate during the pandemic and the ethical considerations guiding them. Methods: Using a qualitative case study approach, we aimed to document the type of ethical decision, reasoning used, and the action frontline HCPs took during the pandemic. Twenty-five semi-structured interviews were conducted with multi-disciplinary HCPs employed in a single community Intensive Care Unit (ICU) in Ontario. Resource allocation decisions were extracted from the transcripts and were analyzed using conventional content analysis. Results: Resource allocation decisions within critical care practice were ubiquitous and diverse. The constraints imposed by the pandemic and multiple provincial and organizational policies formed the context that necessitated these decisions. HCPs drew upon a range of ethical theories, notably Utilitarianism and Virtue Ethics, while prioritizing HCP safety and patient well-being. Resulting actions included prioritizing clinical tasks, establishing boundaries, and adapting practice patterns. Although these situations commonly evoked stress and frustration amongst HCPs, some positive internal responses were also described, including feelings of self-efficacy, resourcefulness, and team cohesion. Conclusion: In conclusion, analysis of resource allocation-derived decision-making illuminated a variety of challenges that HCPs faced during the COVID-19 pandemic, driven by institutional policies and pragmatic limitations. Insights from this study underscore how these ethical decisions are an inherent part of clinical practice and have the potential to foster positive professional development amidst adversity.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".