The Impact of Stroke Public Awareness Campaigns Differs Between Sociodemographic Groups
Bibliographic record
Abstract
BACKGROUND: Prehospital delays are a major obstacle to timely reperfusion therapy in acute ischemic stroke. Stroke sign recognition, however, remains poor in the community. We present an analysis of repeated surveys to assess the impact of Face, Arm, Speech, Time (FAST) public awareness campaigns on stroke knowledge. METHODS: Four cross-sectional surveys were conducted between July 2016 and January 2019 in the province of Quebec, Canada (n = 2,451). Knowledge of FAST stroke signs (face drooping, arm weakness and speech difficulties) was assessed with open-ended questions. A bilingual English/French FAST public awareness campaign preceded survey waves 1-3 and two campaigns preceded wave 4. We used multivariable ordinal regression models weighted for age and sex to assess FAST stroke sign knowledge. RESULTS: We observed an overall significant improvement of 26% in FAST stroke sign knowledge between survey waves 1 and 4 (odds ratio [OR] = 1.26; 95% CI: 1.02, 1.55; p = 0.035). After the last campaign, however, 30.5% (95% CI: 27.5, 33.6) of people were still unable to name a single FAST sign. Factors associated with worse performance were male sex (OR = 0.68; 95% CI: 0.53, 0.86; p = 0.002) and retirement (OR = 0.54; 95% CI: 0.35, 0.83; p = 0.005). People with lower household income and education had a tendency towards worse stroke sign knowledge and were significantly less aware of the FAST campaigns. CONCLUSIONS: Knowledge of FAST stroke signs in the general population improved after multiple public awareness campaigns, although it remained low overall. Future FAST campaigns should especially target men, retired people and individuals with a lower socioeconomic status.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".