Public health campaigns and their effect on stroke knowledge in a high-risk urban population: A five-year study
Bibliographic record
Abstract
Background The level of knowledge of stroke risk factors and stroke symptoms within a population may determine their ability to recognize and ultimately react to a stroke. Independent agencies have addressed this through extensive awareness campaigns. The aim of this study was to determine the change in baseline knowledge of stroke risk factors, symptoms, and source of stroke knowledge in a high-risk Toronto population between 2010 and 2015. Methods Questionnaires were distributed to adults presenting to cardiovascular clinics at the University of Toronto in Toronto, Canada. In 2010 and 2015, a total of 207 and 818 individuals, respectively, participated in the study. Participants were identified as stroke literate if they identified (1) at least one stroke risk factor and (2) at least one stroke symptom. Results A total of 198 (95.6%) and 791 (96.7%) participants, respectively, completed the questionnaire in 2010 and 2015. The most frequently identified risk factors for stroke in 2010 and 2015 were, respectively, smoking (58.1%) and hypertension (49.0%). The most common stroke symptom identified was trouble speaking (56.6%) in 2010 and weakness, numbness or paralysis (67.1%) in 2015. Approximately equal percentages of respondents were able to identify ≥1 risk factor (80.3% vs. 83.1%, p = 0.34) and ≥1 symptom (90.9% vs. 88.7%, p = 0.38). Overall, the proportion of respondents who were able to correctly list ≥1 stroke risk factors and stroke symptoms was similar in both groups.(76.8% vs. 75.5%, p = 0.70). The most commonly reported stroke information resource was television (61.1% vs. 67.6%, p = 0.09). Conclusion Stroke literacy has remained stable in this selected high-risk population despite large investments in public campaigns over recent years. However, the baseline remains high over the study period. Evaluation of previous campaigns and development of targeted advertisements using more commonly used media sources offer opportunities to enhance education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".