Breast cancer patients and survivors: perceived safety and understanding about the COVID-19 vaccine and impact of the pandemic on treatment and follow-up
Bibliographic record
Abstract
Breast cancer is the most prevalent cancer worldwide. When COVID-19 vaccines were first approved, clinical-trials specifically involving cancer patients were lacking and research on efficacy was not available. Thus, the purposes of this study were to investigate perceived safety and understanding toward COVID-19 vaccination amongst breast cancer patients and survivors, and explore the impact of the pandemic on cancer treatment and follow-ups. A web-based survey was sent to breast cancer patients or survivors in Saskatchewan, Canada between September and November 2021. Main questions on safety included participant perceived safety of the COVID-19 vaccine and vaccination status and for understanding it was how well informed they felt about the vaccine. For analysis, descriptive statistics, regression models and Spearman’s rank correlation coefficients were used ( p < 0.05). Among the 92 female participants, 96% had received the vaccine and 85% felt safe about it. Although most respondents felt adequately informed about the vaccine (> 80%), 48% expressed concerns about vaccine. Higher income and recent diagnosis were associated with perceived safety (p = 0.004 and p = 0.003 respectively), and recent diagnosis was also associated with increased perceived understanding about the vaccine (p = 0.042). Additionally, disruptions in treatments (27%) and follow-up care (40%) due to the pandemic were reported. Perceived safety and understanding of the COVID-19 vaccine were overall very positive among participants. However, strategies aimed at addressing concerns about vaccinations and reducing disruption of treatment and follow-up could be improved in future pandemics.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".