Physician preparedness for resource allocation decisions under pandemic conditions: A cross-sectional survey of Canadian physicians, April 2020
Bibliographic record
Abstract
BACKGROUND: Under the pandemic conditions created by the novel coronavirus of 2019 (COVID-19), physicians have faced difficult choices allocating scarce resources, including but not limited to critical care beds and ventilators. Past experiences with severe acute respiratory syndrome (SARS) and current reports suggest that making these decisions carries a heavy emotional toll for physicians around the world. We sought to explore Canadian physicians' preparedness and attitudes regarding resource allocation decisions. METHODS: From April 3 to April 13, 2020, we conducted an 8-question online survey of physicians practicing in the region of Ottawa, Ontario, Canada, organized around 4 themes: physician preparedness for resource rationing, physician preparedness to offer palliative care, attitudes towards resource allocation policy, and approaches to resource allocation decision-making. RESULTS: We collected 219 responses, of which 165 were used for analysis. The majority (78%) of respondents felt "somewhat" or "a little prepared" to make resource allocation decisions, and 13% felt "not at all prepared." A majority of respondents (63%) expected the provision of palliative care to be "very" or "somewhat difficult." Most respondents (83%) either strongly or somewhat agreed that there should be policy to guide resource allocation. Physicians overwhelmingly agreed on certain factors that would be important in resource allocation, including whether patients were likely to survive, and whether they had dementia and other significant comorbidities. Respondents generally did not feel confident that they would have the social support they needed at the time of making resource allocation decisions. INTERPRETATION: This rapidly implemented survey suggests that a sample of Canadian physicians feel underprepared to make resource allocation decisions, and desire both more emotional support and clear, transparent, evidence-based policy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".