Differences in the perception of blood transfusion risk between laypeople and physicians
Bibliographic record
Abstract
BACKGROUND: There is little objective evidence to support the commonly held belief that laypeople perceive blood transfusion risk differently from physicians. Acknowledging and characterizing such differences may improve risk communication. The objective of this study was to characterize how laypeople and physicians perceive the risks of blood transfusion in comparison with a wide variety of other hazards. STUDY DESIGN AND METHODS: A total of 161 laypeople and 91 physicians and medical trainees were surveyed in Kingston, Ontario, between March and August 2000. The perceived riskiness and other qualitative characteristics of blood transfusion and 9 other hazards were measured by psychometric scaling and principal components analysis. RESULTS: The overall return rate was 100 percent, with 86 percent of surveys having no missing responses. Physicians perceived the risks of blood transfusion and most other hazards to be less dreaded and severe, but also less understood and controllable than laypeople. CONCLUSION: Laypeople and physicians perceive risk differently for blood transfusion, but this perceptual gap between the groups for blood transfusion may be representative of a more generalized phenomenon that spans different types of hazards, both medical and nonmedical. Awareness of such differences may facilitate risk communication and shared decision making between physicians and their patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".