The impact of delayed nonurgent surgery during the COVID-19 pandemic on surgeons in Alberta: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, nonurgent surgeries were delayed to preserve capacity for patients admitted with COVID-19; surgeons were challenged personally and professionally during this time. We aimed to describe the impact of delays to nonurgent surgeries during the COVID-19 pandemic from the surgeons' perspective in Alberta. METHODS: We conducted an interpretive description qualitative study in Alberta from January to March 2022. We recruited adult and pediatric surgeons via social media and through personal contacts from our research network. Semistructured interviews were conducted via Zoom, and we analyzed the data via inductive thematic analysis to identify relevant themes and subthemes related to the impact of delaying nonurgent surgery on surgeons and their provision of surgical care. RESULTS: We conducted 12 interviews with 9 adult surgeons and 3 pediatric surgeons. Six themes were identified: accelerator for a surgical care crisis, health system inequity, system-level management of disruptions in surgical services, professional and interprofessional impact, personal impact, and pragmatic adaptation to health system strain. Participants also identified strategies to mitigate the challenges experienced due to nonurgent surgical delays during the COVID-19 pandemic (i.e., additional operating time, surgical process reviews to reduce inefficiencies, and advocacy for sustained funding of hospital beds, human resources and community-based postoperative care). INTERPRETATION: Our study describes the impacts and challenges experienced by adult and pediatric surgeons of delayed nonurgent surgeries because of the COVID-19 pandemic response. Surgeons identified potential health system-, hospital- and physician-level strategies to minimize future impacts on patients from delays of nonurgent surgery.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".