Personalized risk communication for personalized risk assessment: Real world assessment of knowledge and motivation for six mortality risk measures from an online life expectancy calculator
Bibliographic record
Abstract
In the clinical setting, previous studies have shown personalized risk assessment and communication improves risk perception and motivation. We evaluated an online health calculator that estimated and presented six different measures of life expectancy/mortality based on a person's sociodemographic and health behavior profile. Immediately after receiving calculator results, participants were invited to complete an online survey that asked how informative and motivating they found each risk measure, whether they would share their results and whether the calculator provided information they need to make lifestyle changes. Over 80% of the 317 survey respondents found at least one of six healthy living measures highly informative and motivating, but there was moderate heterogeneity regarding which measures respondents found most informative and motivating. Overall, health age was most informative and life expectancy most motivating. Approximately 40% of respondents would share the results with their clinician (44%) or social networks (38%), although the information they would share was often different from what they found informative or motivational. Online personalized risk assessment allows for a more personalized communication compared to historic paper-based risk assessment to maximize knowledge and motivation, and people should be provided a range of risk communication measures that reflect different risk perspectives.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".