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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".