Evaluation of a visual risk communication tool: effects on knowledge and perception of blood transfusion risk
Bibliographic record
Abstract
BACKGROUND: Effective risk communication in transfusion medicine is important for health-care consumers, but understanding the numerical magnitude of risks can be difficult. The objective of this study was to determine the effect of a visual risk communication tool on the knowledge and perception of transfusion risk. STUDY DESIGN AND METHODS: Laypeople were randomly assigned to receive transfusion risk information with either a written or a visual presentation format for communicating and comparing the probabilities of transfusion risks relative to other hazards. Knowledge of transfusion risk was ascertained with a multiple-choice quiz and risk perception was ascertained by psychometric scaling and principal components analysis. RESULTS: Two-hundred subjects were recruited and randomly assigned. Risk communication with both written and visual presentation formats increased knowledge of transfusion risk and decreased the perceived dread and severity of transfusion risk. Neither format changed the perceived knowledge and control of transfusion risk, nor the perceived benefit of transfusion. No differences in knowledge or risk perception outcomes were detected between the groups randomly assigned to written or visual presentation formats. CONCLUSION: Risk communication that incorporates risk comparisons in either written or visual presentation formats can improve knowledge and reduce the perception of transfusion risk in laypeople.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".