Implementation and public acceptability: lessons from food irradiation and how they might apply to pathogen reduction in blood products
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The issues around food irradiation (FI) have both similarities and differences to pathogen reduction (PR) in blood products. We performed a systematic search of the FI literature to identify lessons that could help to inform the implementation of pathogen reduction technology for blood products. METHODS: A comprehensive literature search was performed in EMBASE. MEDLINE, PSYCHINFO, CINAL and Physiological Abstracts for articles related to FI that met predefined eligibility criteria. A coding scheme was developed by the investigators, and relevant information from the articles was coded using NVivo 9. Reports for each code were generated and summarized. RESULTS: One thousand two hundred and sixty-six articles were identified by the broad search, and 50 met the study eligibility criteria for inclusion. The implementation of FI was slow and has been met by significant controversy, sparked by concerns from the public and social groups about the acceptability of irradiated food. Numerous factors influenced public acceptability including: demographic factors; perceptions of safety and risk; endorsement of and trust in the FI industry and social institutions that serve as opinion leaders; knowledge and the provision of scientific information including benefits and cost; and the availability of choice. CONCLUSION: There are a number of lessons from the FI literature that may be generalizable to the implementation of PR of blood products. Based on findings from this study, six recommendations are made to facilitate public implementation of this new technology.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".