Perception of blood donation among employees of healthcare organizations during <scp>COVID</scp>‐19 pandemic: A national multicenter cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Maintaining a safe and adequate blood supply during a crisis is a major challenge facing blood banks around the world. With the recent global COVID-19 crisis and the enforced "stay at home" lockdown, access to blood donors was limited. Since employees of healthcare facilities may act as potential blood donors, their perception of blood donation and their willingness to donate during the pandemic period is important to be assessed. STUDY DESIGN AND METHOD: A national cross-sectional study at six centers in Saudi Arabia was conducted using an online-based questionnaire that was distributed to all healthcare employees in these facilities between June and August 2020. RESULTS: Among the total of 1664 participants, 63.2% (n = 1051) did not donate blood during the last 2 years. However, 53% (n = 882) of participants reported they are likely to donate blood during the COVID-19 crisis. Furthermore, 85% (n = 1424) did not donate blood during the current pandemic, with the biggest worries of getting the COVID-19 infection in the donor center. The main concerns of participants were about adherence to physical distancing requirements and the safety of the donation procedure. The majority of health care participants (88.2%) support implementing a hospital policy for a voluntary blood donation by employees during crises. CONCLUSION: Recruitment of more blood donors among health care employees is a feasible solution to improve the blood supply during a crisis. This should be based on efforts throughout the year including regular awareness campaigns and effective communication.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".