The impact of the COVID-19 pandemic on health-related quality of life of cancer patients in British Columbia
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic resulted in unprecedented changes to cancer care in many countries, impacting cancer patients' lives in numerous ways. This study examines the impact of changes in cancer care on patient's health-related quality of life (HRQL), which is a key outcome in cancer care. The study aims to estimate patients' self-reported HRQL before and during the pandemic and identify predictive factors for their physical and mental wellbeing. METHOD: The study employed the large-scale Outpatient Cancer Care (OCC) Patient Experience Survey, including the Veterans RAND 12-Item Health Survey, to evaluate cancer patients' experiences and HRQL before (January to May 2020) and during the COVID-19 pandemic (May to July 2021). Paired t-tests were conducted to compare differences in Physical Component Scores (PCS) and Mental Component Scores (MCS) before and during the pandemic. Multivariable linear regressions were employed to investigate the factors (sociodemographic, clinical, and patient-reported experience) influencing PCS and MCS during the pandemic. RESULTS: PCS decreased significantly during the pandemic, while MCS remained stable. Lower PCS contributors included older age, more telehealth visits, self-reported hospitalization, and a longer time since the last cancer diagnosis. Higher PCS was associated with urban residence, higher MCS during the pandemic, and perceived active Healthcare Provider (HCP) involvement. For MCS, lower scores related to female gender and more telehealth visits, while higher scores were associated with being white, higher education, high MCS before the pandemic, and perceived active HCP involvement. CONCLUSION: The OCC Patient Experience Survey provides a unique patient level data set measuring HRQL pre- and post- the onset of the COVID-19 pandemic. The study highlights challenges faced by cancer patients during the pandemic, with a significant reduction in PCS. However, the stability in MCS suggests effective coping mechanisms. Sociodemographic, clinical, and telehealth-related variables play a complex role in shaping both PCS and MCS. Perceived HCP involvement emerges as a crucial factor correlating with higher PCS and MCS. Navigating the post-pandemic era necessitates interventions fortifying patient-provider relationships, optimizing healthcare support systems, such as telehealth services, and prioritizing mental-well-being given its impact on both PCS and MCS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".