Resource allocation decisions under pandemic conditions: A cross-sectional survey of Ontario physicians
Bibliographic record
Abstract
The COVID-19 pandemic has forced physicians to confront difficult choices regarding the allocation of scarce resources, such as ventilators and critical care beds. Developing policies to guide the allocation of such resources has proven challenging. An understanding of physicians' attitudes and beliefs surrounding resource allocation could help inform policymaking. As a replication and extension of a survey of Ottawa physicians conducted in 2020, we surveyed physicians across Ontario, Canada in April 2021. This survey examined physicians' sense of preparedness to allocate critical care resources during the pandemic, attitudes concerning resource allocation policy, and approaches to resource allocation decision-making. Of the 253 responses included for analysis, the majority (67%) of respondents indicated feeling "somewhat" or "a little prepared" to make resource allocation decisions, while 20% indicated feeling "not at all prepared." Most respondents (86%) agreed that a policy to guide resource allocation in the event of scarcity should exist. Physicians overwhelmingly agreed that important factors to consider when making resource allocation decisions included the patient likelihood of survival, frailty index, comorbidities, and cognitive status. Responses from the province-wide survey conducted in 2021 resemble the results of an analogous survey of Ottawa physicians conducted in 2020. Physicians generally felt underprepared to make resource allocation decisions and agreed that official policies should guide such decisions. Identification of factors relevant to resource allocation was remarkably consistent across this sample and that taken in 2020.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".