Comparison of Stroke Warning Sign Campaigns in Australia, England, and Canada
Bibliographic record
Abstract
BACKGROUND: Public awareness of the signs of stroke is essential to ensure that those affected by stroke arrive at the hospital in time for lifesaving therapies. It is unclear how well stroke awareness campaigns improve awareness of stroke signs and whether people translate this into action. METHODS: We evaluated stroke awareness campaigns conducted in England, Australia, and Canada using pre- and post-campaign surveys. We assessed the proportion of people who could name the main signs of stroke, and compared the proportion naming these correctly between locations. We also assessed whether people would call emergency services in the event of a stroke. Proportion responding correctly was compared using chi-square analysis. RESULTS: The amount spent on the campaigns was different in each country. The post-campaign survey was conducted among 400 people in Australia, 1921 in England, and 2703 in Canada. Sixty-eight per cent of people in Australia and 57% in Canada could name two or more signs of stroke (P < 0.001). After the campaign, knowledge of each of the elements of the campaign (face, arm, speech, time) was significantly greater in England than in Australia (P < 0.001 for each item). A high proportion of participants reported that they would call emergency services in the event of a stroke (97% in England, 90% in Australia, and 67% in Canada). CONCLUSION: Knowledge of stroke signs and the action to be taken can be improved with awareness campaigns. The effectiveness of these campaigns may be enhanced by spend on media, media mix, and key messages. It is critical to ensure that campaigns provide the clear and bold message that prompt action is an essential ingredient to reduce death and disability following stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".