COVID‐19 and patients with cancer: Investigating treatment impact, information sources, and COVID‐19–related knowledge, attitudes, and practices
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused enormous strain on public health. Patients with cancer are particularly susceptible to the disease, and their treatment plans have been threatened by public health restrictions designed to contain the spread. METHODS: This study examined the effects of the pandemic on cancer patients' psychology, knowledge, attitudes, and practices concerning COVID-19 as well as their perceptions of the impact of COVID-19 on their cancer health care services. A survey was sent to 5800 patients at a cancer center in Toronto, Canada. Descriptive results were summarized. Qualitative feedback was coded and summarized. To examine for potential associations, regression models were tested for the outcomes of patient psychological well-being, knowledge, attitudes, and practices, and they accounted for several demographic, health literacy, and disease variables. RESULTS: A total of 1631 surveys were completed. Most patients saw their appointments shifted to virtual visits, and for a substantial minority, there was no change. A majority of the patients (62%) expressed fears about contracting the virus. There were no independent predictors of COVID-19-related knowledge. Fears were more pronounced among patients who did not speak English and those who used social media more often. Female participants, those who scored higher on knowledge questions, and those who used cancer center materials were more likely to take preventative measures against infection. CONCLUSIONS: This study provides a snapshot of the state of cancer patient treatment and the knowledge, attitudes, and practices of patients between the first 2 waves of the pandemic. The study's results can inform our understanding of adaptation to conditions during and after the outbreak.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".