Biopsychosocial Associates of Psychological Distress and Post-Traumatic Growth among Canadian Cancer Patients during the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Understanding both the positive and negative psychological outcomes among cancer patients during the pandemic is critical for planning post-pandemic cancer care. This study (1) examined levels of psychological distress and post-traumatic growth (PTG) among Canadian cancer patients during the COVID-19 pandemic and (2) explored variables that were associated with psychological distress and PTG during the pandemic using a biopsychosocial framework. METHOD: A cross-section survey was undertaken of patients receiving ongoing care at a regional cancer centre in Ontario, Canada, between February and December 2021. Self-reported questionnaires assessing sociodemographic information, social difficulties, psychological distress (depression, anxiety fear of recurrence, and emotional distress), PTG, illness perceptions, and behavioural responses to the pandemic were administered. Disease-related information was extracted from patient health records. RESULTS: = 104), respectively. Approximately 43% of the sample reported experiencing high PTG, and these positive experiences were not associated with levels of distress. Social factors, including social difficulties, being female, lower education, and unemployment status were prominent associative factors of patient distress. Perceptions of the pandemic as threatening, adopting more health safety behaviours, and not being on active treatment also increased patient likelihood to experience severe psychological distress. Younger age and adopting more health safety behaviours increased the likelihood of experiencing high PTG. The discriminatory power of the predictive models was strong, with a C-statistic > 0.80. CONCLUSIONS: Examining both the positive and negative psychological patient outcomes during the pandemic has highlighted the complex range of coping responses. Interventions that adopt a multi-pronged approach to screen and address social distress, as well as to leverage health safety behaviours, may improve the adjustments in the pandemic aftermath.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".