Delivery and Prioritization of Surgical Care in Canada During COVID-19: An Environmental Scan
Bibliographic record
Abstract
BACKGROUND: During COVID-19 healthcare systems had to make concessions to make room for the surge of COVID-19 patients requiring hospital and intensive care. Postponing surgeries was a common strategy; however, it is unclear how surgical care was delivered during this time of constraint. The objective of this study was to understand how surgical care was delivered and prioritized during the COVID-19 pandemic response. METHODS: This was an environmental scan following the Canadian Agency for Drugs and Technologies in Health methodology. This study was conducted in Canada; a universal, publicly funded healthcare system. Evidence sources on policies pertaining to the provision of surgical care between January 2020 and October 2022 were obtained from ministries of health, health services agencies and publicly funded hospitals across all 10 provinces and three territories. We synthesized the evidence sources using framework analysis. RESULTS: We identified 205 evidence sources that described six themes about the provision of surgical care during the COVID-19 pandemic: the cycle of postponement and resumption; guidelines for triaging and prioritizing surgical cases; Infection Prevention and Control (IPAC), and safety measures for surgical care during COVID-19, patient-centred care, and looking forward (recovery planning, leadership, and decision-making). CONCLUSION: This study provides a comprehensive understanding of how surgical care was disrupted and innovated during COVID-19 which can inform future strategies for providing effective and efficient surgical care during times of healthcare constraint.
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 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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".