The Association Between Fear of COVID-19 and Mental Health Outcomes Among Canadians Living with Cancer During the COVID-19 Pandemic
Bibliographic record
Abstract
Cancer patients have been uniquely impacted by the COVID-19 pandemic with cancer care changes, increased infection risk, and social isolation indicating continued risk to the mental wellness of this population. Before the COVID-19 pandemic began, the relationship between anxiety and depression with poor health outcomes for cancer patients was well researched indicating that 24% of Canadian cancer patients experience both anxiety and depression (Carlson et al., 2004). Preliminary research from the pandemic indicated that over 44% of Canadian cancer patients have experienced some degree of anxiety; a proportion significantly higher than the pre-COVID prevalence (DiMatteo et al., 2000, Massicotte et al., 2021). The objectives of the present study were to determine if the fear of COVID-19 (FCoV) was associated with psychosocial distress in patients living with cancer. A cross-sectional approach was used to analyze survey results from the (CCTG)-led SC.27 study: Living with Cancer in the time of COVID-19. Data from baseline survey responses provided data on FCoV, anxiety and depression outcomes, resilience levels, and other covariates of interest. The association between FCoV and i) anxiety and ii) depression was assessed using multiple logistic regression with backwards deletion to generate adjusted odds ratios and 95% confidence intervals. Approximately 500 participants who consented and completed the baseline SC.27 survey were included in the final analyses (n=511 and n=514 for FCoV and anxiety and depression analyses, respectively). Elevated FCoV was independently associated with both anxiety (OR=4.3; 95% CI: 2.3-8.2) and depression (OR=2.8; 95% CI: 1.5-5.3) among cancer patients during the pandemic. The association between FCoV and i) anxiety and ii) depression was attenuated among participants with high resilience, however these interactions did not quite reach statistical significance (p-values =0.052 and 0.09, respectively). The current study fills gaps in the literature that examine the relationship between FCoV with anxiety and depression among cancer patients and is one of the first to do so using validated scales. The association of FCoV with worse anxiety and depression amongst cancer patients, and the possibility of resilience to moderate this relationship, suggest the need for enhanced programming to increase resilience levels among cancer patients during future pandemics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".