Stroke risk perception among participants of a stroke awareness campaign
Bibliographic record
Abstract
Background: Subjective risk factor perception is an important component of the motivation to change unhealthy life styles. While prior studies assessed cardiovascular risk factor knowledge, little is known about determinants of the individual perception of stroke risk. Methods: Survey by mailed questionnaire among 1483 participants of a prior public stroke campaign in Germany. Participants had been informed about their individual stroke risk based on the Framingham stroke risk score. Stroke risk factor knowledge, perception of lifetime stroke risk and risk factor status were included in the questionnaire, and the determinants of good risk factor knowledge and high stroke risk perception were identified using logistic regression models. Results: Overall stroke risk factor knowledge was good with 67-96% of the participants recognizing established risk factors. The two exceptions were diabetes (recognized by 49%) and myocardial infarction (57%). Knowledge of a specific factor was superior among those affected by it. 13% of all participants considered themselves of having a high stroke risk, 55% indicated a moderate risk. All major risk factors contributed significantly to the perception of being at high stroke risk, but the effects of age, sex and education were non-significant. Poor self-rated health was additionally associated with high individual stroke risk perception. Conclusion: Stroke risk factor knowledge was high in this study. The self perception of an increased stroke risk was associated with established risk factors as well as low perception of general health
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".