Psychosocial Distress in Adult Patients Awaiting Cancer Surgery during the COVID-19 Pandemic
Bibliographic record
Abstract
Cancer causes substantial emotional and psychosocial distress, which may be exacerbated by delays in treatment. The COVID-19 pandemic has resulted in increased wait times for many patients with cancer. In this study, the psychosocial distress associated with waiting for cancer surgery during the pandemic was investigated. This cross-sectional, convergent mixed-methods study included patients with lower priority disease during the first wave of COVID-19 at an academic, tertiary care hospital in eastern Canada. Participants underwent semi-structured interviews and completed two questionnaires: Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). Qualitative analysis was completed through a thematic analysis approach, with integration achieved through triangulation. Fourteen participants were recruited, with cancer sites including thyroid, kidney, breast, prostate, and a gynecological disorder. Increased anxiety symptoms were found in 36% of patients and depressive symptoms in 14%. Similarly, 64% of patients experienced moderate or high stress. Six key themes were identified, including uncertainty, life changes, coping strategies, communication, experience, and health services. Participants discussed substantial distress associated with lifestyle changes and uncertain treatment timelines. Participants identified quality communication with their healthcare team and individualized coping strategies as being partially protective against such symptoms. Delays in surgery for patients with cancer during the COVID-19 pandemic resulted in extensive psychosocial distress. Patients may be able to mitigate these symptoms partially through various coping mechanisms and improved communication with their healthcare teams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".