Psychosocial Wellbeing among Patients with Breast Cancer during COVID-19
Bibliographic record
Abstract
The impact of coronavirus disease 2019 (COVID-19) on the wellbeing of breast cancer (BC) patients is not well understood. This study described psychosocial problems among these patients in the United States (US) during the COVID-19 pandemic. Data were collected from BC patients via an online self-report survey between 30 March–6 July 2021 to assess the prevalence of COVID-19 diagnosis history and potential depression, health-related quality of life, COVID-related stress, and financial toxicity. Patients with early-stage (eBC) and metastatic (mBC) disease were compared. Of 669 patients included in the analysis, the prevalence of COVID-19 diagnosis history (10.9% versus 7.7%) and potential depression (33.7% versus 28.3%) were higher in mBC than eBC patients. Patients with eBC (versus mBC) had higher scores on nearly all Functional Assessment of Cancer Therapy-Breast scales (all, p < 0.001). For the Psychological Impact of Cancer subscales measuring negative coping strategies, the emotional distress score was the highest (9.1 ± 1.8) in the overall sample. Comprehensive Score for Financial Toxicity scores were higher in eBC than in mBC patients (24.2 ± 11.3 vs. 21.3 ± 10.2, p < 0.001). Overall, the COVID-19-related stress score was highest for danger/contamination fears (8.2 ± 5.6). In conclusion, impairments to psychosocial wellbeing among patients during the pandemic were observed, particularly financial toxicity and poor mental health and emotional functioning, with greater problems among mBC patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".