Public Perceptions of Pathogen Reduction Technology in the Canadian Donor Blood Supply
Bibliographic record
Abstract
Background The safety of the donor blood supply has been a major public health concern to the Canadian public in the past. Multiple safety measures are in place to ensure its continued safety, including a risk‐based decision‐making process associated with blood‐borne pathogen transmission. Pathogen reduction technology (PRT) may provide such an approach by inactivating pathogens in donated blood to prevent spread to a blood transfusion recipient. To date, no research has assessed the Canadian public's opinions of PRT, which can aid in policy development and implementation strategies. Study Design and Methods This paper details the results of an email survey that was developed around awareness of blood system practices in Canada, opinions of PRT and health risk perceptions to the Canadian public. 1008 individuals from Ontario and Québec responded to the survey. Results Results indicated that there is a generally low reported knowledge of PRT amongst the Canadian public. Regression analysis revealed that education, employment and prior blood donation have an effect on awareness of PRT. Conclusions PRT is often met with mixed opinions. The majority of those surveyed agree with its use, yet many were hesitant about the chemical aspect. This also indicates minimal knowledge about PRT and may additionally be a result of the low perceived risk of blood transfusions to the Canadian public. An increase in public awareness around PRT risk may be beneficial in the future if this technology is to be implemented.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".