Comparing the Standard Rating Scale and the Magnifier Scale for Assessing Risk Perceptions
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: A new risk perception rating scale ("magnifier scale") was recently developed to reduce elevated perceptions of low-probability health events, but little is known about its performance. The authors tested whether the magnifier scale lowers risk perceptions for low-probability (in 0%-1% magnifying glass section of scale) but not high-probability (>1%) events compared to a standard rating scale (SRS). METHOD: In studies 1 (n = 463) and 2 (n = 105), undergraduates completed a survey assessing risk perceptions of high- and low-probability events in a randomized 2 x 2 design: in study 1 using the magnifier scale or SRS, numeric risk information provided or not, and in study 2 using the magnifier scale or SRS, high- or low-probability event. In study 3, hypertension patients at the Philadelphia Veterans Affairs hospital completed a similar survey (n = 222) assessing risk perceptions of 2 self-relevant high-probability events-heart attack and stroke-with the magnifier scale or the SRS. RESULTS: In study 1, when no risk information was provided, risk perceptions for both high- and low-probability events were significantly lower (P < 0.0001) when using the magnifier scale compared to the SRS, but risk perceptions were no different by scale when risk information was provided (interaction term: P = 0.003). In studies 2 and 3, risk perceptions for the high-probability events were significantly lower using the magnifier scale than the SRS (P = 0.015 and P = 0.014, respectively). CONCLUSIONS: The magnifier scale lowered risk perceptions but did so for low- and high-probability events, suggesting that the magnifier scale should not be used for assessments of risk perceptions for high-probability events.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it