COVID-19 and Cancer Patients in the Second Year of the Pandemic: Investigating Treatment Impact, Information Sources, and COVID-19-Related Knowledge, Attitudes and Practices
Bibliographic record
Abstract
Background: The novel coronavirus that has triggered the present COVID-19 pandemic continues to spread globally, resulting in widespread morbidity and mortality. Patients with cancer remain one of the most vulnerable subsets of the population to the disease. This study examined the effects of the pandemic on cancer patients’ treatment, psychology, knowledge, attitudes, and practices. Methods: A survey was emailed to 9861 patients at a cancer centre in Toronto, Canada. Descriptive results were summarized. Qualitative feedback was coded and summarized. Regression modelling was used to explore factors associated with patient psychological well-being, knowledge, attitudes, and practices. Results: A total of 1760 surveys were completed, with a response rate of 17.8%. Most participants did not experience any pandemic-related treatment delays, and vaccination rates were high. Participants who identified themselves as non-white (OR 3.30, CI: 1.30–5.30; p ≤ 0.001), and those who referred to journal articles for information (p = 0.002) reported higher psychological impact scores. There were no significant predictors of whether participants would use personal protective equipment when leaving their homes or whether they would go to crowded places. Discussion: This study provides another snapshot of cancer patients perceptions and needs during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".