Patients’ experiences undergoing cancer surgery during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
Abstract Purpose: This study aimed to understand patients’ experiences undergoing cancer surgery during the COVID-19 pandemic. In response to COVID-19, many elective cancer surgeries were delayed creating a massive backlog of cases. Patients’ experiences with surgical delays may inform healthcare systems’ responses to the backlog of cases and guide preparations for future healthcare emergencies. Methods: This was a qualitative description study. Patients undergoing general surgery for cancer at two university-affiliated hospitals between March 2020 and January 2021 were invited to one-to-one interviews. Patients were purposefully selected using quota sampling until interviews produced no new information (i.e., thematic saturation). Interviews were conducted using a semi-structured guide and analyzed according to inductive thematic analysis. Results: Twenty patients were included [mean age 64±12.9; male (n=10); surgical delay (n=14); cancer sites: breast (n=8), skin (n=4), hepato-pancreato-biliary (n=4), colorectal (n=2), and gastro-esophageal (n=2)]. When determining their willingness to undergo surgery, patients weighed the risk of COVID-19 infection against the urgency of their disease. Changes to the hospital environment (e.g., COVID-19 preventative measures) and deviations from expected treatment (e.g., alternative treatments, remote consultations, rescheduled care) caused diverse psychological responses, ranging from increased satisfaction to severe distress. Patients employed several coping strategies to mitigate distress, including eliciting reassurance from care providers, seeking information from unconventional sources, and reframing care interruptions. Conclusions: Changes in care during the pandemic elicited diverse psychological responses from patients undergoing cancer surgery. Coping was facilitated by consistent communication with providers, emphasizing the importance of patient-centered expectation setting as we prepare for the future within and beyond the pandemic.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".