Aiding Risk Information learning through Simulated Experience (ARISE): A Comparison of the Communication of Screening Test Information in Explicit and Simulated Experience Formats
Bibliographic record
Abstract
OBJECTIVE: To determine whether the use of Aiding Risk Information learning through Simulated Experience (ARISE) to communicate conditional probabilities about maternal serum screening results for Down syndrome promotes more accurate positive predictive value (PPV) estimates and conceptual understanding of screening, compared with explicitly providing individuals with this information via numerical summary or icon array. METHOD: In experiment 1, 582 participants completed an online study in which they were asked to estimate the PPV and rate their attitudes toward a screening test when information was presented in either a description (required calculation of the PPV), explicit (PPV was provided and had to be identified), or an ARISE format (PPV was inferred through experience-based learning). In experiment 2, 316 participants estimated the PPV and rated their attitudes toward screening based on information presented in either an icon array (identify the icons that represent the PPV) or ARISE format. RESULTS: In experiment 1, ARISE elicited the most accurate PPV estimates compared with the description and explicit formats, and both the explicit and ARISE formats led to more unfavorable attitudes toward screening. In experiment 2, both the icon array and ARISE resulted in similar PPV estimates; however, ARISE led to more negative attitudes toward screening. CONCLUSIONS: These findings suggest that ARISE may be superior to other formats in the communication of PPV information for screening tests. However, differences in the complexity of the formats vary and require further investigation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".