COVID-19 Pandemic Stressors and Psychological Symptoms in Breast Cancer Patients
Bibliographic record
Abstract
Background. The current Coronavirus disease 2019 (COVID-19) pandemic is a highly stressful event that may lead to significant psychological symptoms, particularly in cancer patients who are at a greater risk of contracting viruses. This study examined the frequency of stressors experienced in relation to the ongoing coronavirus pandemic and its relationship with psychological symptoms (i.e., anxiety, depression, insomnia, fear of cancer recurrence) in breast cancer patients. Methods. Thirty-six women diagnosed with a non-metastatic breast cancer completed the Insomnia Severity Index, the Hospital Anxiety and Depression Scale, the severity subscale of the Fear of Cancer Recurrence Inventory, and the COVID-19 Stressors Questionnaire developed by our research team. Participants either completed the questionnaires during (30.6%) or after (69.4%) their chemotherapy treatment. Results. Results revealed that most of the participants (63.9%) have experienced at least one stressor related to the COVID-19 pandemic (one: 27.8%, two: 22.2%, three: 11.1%). The most frequently reported stressor was increased responsibilities at home (33.3%). Higher levels of concerns related to the experienced stressors were significantly correlated with higher levels of anxiety, depressive symptoms, insomnia, and fear of cancer recurrence, rs(32) = 0.36 to 0.59, all ps < 0.05. Conclusions. Cancer patients experience a significant number of stressors related to the COVID-19 pandemic, which are associated with increased psychological symptoms. These results contribute to a better understanding of the psychological consequences of a global pandemic in the context of cancer and they highlight the need to better support patients during such a challenging time.
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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 | 0.000 |
| 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) | 0.002 | 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".