A survey of resource allocation among canadian cardiac surgery programs during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019(COVID-19) pandemic significantly impacted the lives of patients and healthcare professionals globally. With rapid spread and severe illness, a great deal of healthcare resources including personal, funding, and hospital beds were dedicated to fight the pandemic. OBJECTIVES: This survey looks to characterize how resources were allocated among Canadian cardiac surgery programs, and how this impacted patient care and outcomes. METHODS: Canadian cardiac surgeons were identified and asked via email to complete a 24-question survey regarding the impact of resource limitations during the COVID-19 pandemic on their practice, the treatment of their patients, and their outcomes. RESULTS: Twenty-six Canadian cardiac surgeons responded to the survey. The majority of respondents experienced >25 % reductions in elective case volumes(69.1 %) and noted adverse outcomes due surgery delay(92 %). Respondents felt that resource reallocation was required to provide optimal care to COVID-19 patients but also felt that the restrictions negatively impacted the outcomes of their non-COVID-19 patients(88.5 %). CONCLUSIONS: Canadian cardiac surgery programs experienced reduced case volumes, inadequate resources to care for patients, and adverse patient outcomes as a result of limited resources. While the reallocation of personal and hospital space towards the pandemic response was certainly required and pandemic-related restrictions have largely passed, the backlog of surgical cases persists and several lessons can be learned that may help to navigate future times of limited resources. During times of limited resources, an emphasis on allocating resources by policymakers aimed at an overall reduction in morbidity may help to minimize persistent impacts of these events.
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 | 0.002 |
| 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) | 0.000 | 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 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".