Using mass media to teach the warning signs of stroke: the long and the short of it
Bibliographic record
Abstract
OBJECTIVE: Stroke is a major cause of morbidity and mortality and rapid treatment is critical to patient outcomes. This paper looks at the effect of paid television advertising campaigns upon the general public's knowledge of the warning signs of stroke and emergency department (ED) stroke presentations. METHODS: Data for the study includes results of nine random-digit dialing telephone surveys conducted among Ontario adults aged 45 and over. The mean number of ED presentations for all strokes and for transient ischemic attacks (TIA) were obtained from the Registry of the Canadian Stroke Network (RCSN). RESULTS: Polls indicated that long advertising campaigns were associated with significant increases in the public's knowledge of stroke warning signs, while shorter campaigns were associated with much smaller increases. Time (as represented by month) was the single most important factor determining the mean number of ED presentations for total stroke but was not for TIAs. Campaign status (on or off the air) had a strong and significant effect on ED presentations when the advertising campaigns were long; when the advertising campaigns were shortened, there was no campaign effect. CONCLUSIONS: Long, intermittent campaigns are effective in increasing the public's awareness of the warning signs of stroke and may have a significant effect on ED presentations for stroke and TIA. Public awareness of stroke warning signs declines during advertising black-outs, so short campaigns are less effective.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".