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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".