Quality of Life, Anxiety, Depression and Psychological Distress in Patients with Cancer During the COVID 19 Pandemic: A Systematic Review
Bibliographic record
Abstract
Introduction and Objectives: Quality of life (QOL) and psychological wellbeing deteriorate during the COVID 19 pandemic in patients with cancer. Purpose: This study aims to review the current evidence of QOL, anxiety, depression, psychological distress, and their inter-relationship in patients with cancer and survivors during the COVID 19 pandemic. Moreover, this study identifies factors associated with QOL and mental health in patients with cancer and survivors during the COVID 19 pandemic. Methods: An extensive electronic database search was conducted. Articles published in English assessing cancer patients and cancer survivors’ QOL and psychological wellbeing. Results: Twenty-seven articles with 22,134 participants were included. Concerns related to contracting COVID 19, along with potential treatment plans were predictors of impaired QOL. Advanced age, family support, being identified as a male and having less comorbid conditions were associated with the high level of QOL. Delay or change in treatment plan, contact with COVID 19 positive individuals, and emotional vulnerability were found to be independently associated with high levels of anxiety, depression, and distress. Conclusion: Health professionals, caregivers and support services should pay more attention on QOL and psychological wellbeing of the patients with cancer. Counselling sessions, support services should be established to improve their life satisfaction and wellbeing.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".