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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".