“It affects every aspect of your life”: A qualitative study of the impact of delaying surgery during COVID-19
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has overwhelmed healthcare systems, leading many jurisdictions to reduce surgical services to create capacity (beds and staff) to care for the surge of patients with COVID-19. These decisions were made in haste, and little is known about the impact on patients whose surgery was delayed. This study explores the impact of delaying non-urgent surgeries on patients, from their perspective. Methods Using an interpretative description approach, we conducted interviews with adult patients and their caregivers who had their surgery delayed or cancelled during the COVID-19 pandemic in Alberta, Canada. Trained interviewers conducted semi-structured interviews. Interviews were iteratively analyzed by two independent reviewers using an inductive approach to thematic content analysis to understand key elements of the patient experience. Results We conducted 16 interviews with participants ranging from 27 to 75 years of age with a variety of surgical procedures delayed. We identified four interconnected themes: individual-level impacts (physical health, mental health, family and friends, work, quality of life), system-level factors (healthcare resources, communication, perceived accountability/responsibility), unique issues related to COVID-19, and uncertainty. Interpretation The patient-reported impact of having a surgery delayed during the COVID-19 pandemic was diffuse and consequential. While the decision to delay non-urgent surgeries was made to manage the strain on healthcare systems, our study illustrates the consequences of these decisions. We advocate for the development and adoption of strategies to mitigate the burden of distress that waiting for surgery during and after COVID-19 has on patients and their family/caregivers.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".