Designing information materials to reduce blame and build trust in health screening: the roles of stereotype content and perceived control
Bibliographic record
Abstract
OBJECTIVES: This study explores strategies to reduce blame for false results and build trust in population-based cervical screening. False results cause patient frustration, blame, and distrust. To address these issues, we developed a decision aid comprising three components: (1) a journey map that visually explains the screening process, (2) a video featuring a medical scientist discussing uncertainty inherent in screening and her role in testing samples, and (3) a diagram illustrating the testing pathway. METHODS AND MEASURES: Eight hundred women in Ireland were randomly assigned to one of four groups in an additive design: (1) control group viewing standard informational materials; (2) control + journey map; (3) control + journey map + video; (4) control + journey map + video + diagram. Exposure to each component was coded as yes/no, and their effects on blame and trust were assessed. RESULTS: The video reduced blame towards laboratories by improving perceptions of medical scientists' warmth and competence and by increasing perceived uncontrollability of false results. Conversely, the journey map and diagram unexpectedly increased blame and anger, suggesting that transparency alone may not suffice to build trust. CONCLUSIONS: The study underscores the importance of considering social cognitive factors in public health communication strategies.
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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 | 0.010 | 0.049 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".