Comparing the Standard Rating Scale and the Magnifier Scale for Assessing Risk Perceptions
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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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".