Awareness and Community Response To Stroke Signs and Symptoms Following the National ‘Act Fast’ Campaign in An Ethnically Diverse Population
Bibliographic record
Abstract
Abstract Background Evaluation of public education stroke campaigns and behavioral studies to assess emergency response at stroke onset are scarce. We aimed to assess patient’s and bystanders’ foreknowledge of stroke signs and symptoms and their response at stroke onset. We also enquired if ‘Act FAST’ stroke campaign in Qatar contributed to their foreknowledge. Methods In Qatar, the first national stroke awareness campaign, ‘Act FAST’, was launched in May 2015. The study population included a convenience sample of stroke patients admitted to the stroke service in Qatar’s largest tertiary care hospital from November 2015-February 2016. We interviewed patients with acute onset stroke admitted to the stroke unit using a validated questionnaire. If the patient had disabling stroke, we interviewed relatives/bystanders present at stroke onset. The primary outcome was the correct response of calling Emergency Medical Services (EMS), recognizing the possibility of stroke. Results The questionnaire was administered to 165 participants, 142 (86.1%) stroke patients, and 23 (13.9%) bystanders. The mean age of the study population was 52.6 (SD = 11.7), and sex (male-female) ratio was 7:1. Ethnic categories were South-Asian (n = 101, 62.2%), Middle-Eastern (n = 14, 8.5%), Far-Eastern (n = 26, 15.8%), African (n = 16, 9.7%) and Others (n = 8, 4.9%). From the study group, 33 (20.1%) participants had foreknowledge of stroke signs and symptoms, and of these, 27 (16.5%) knew about the Act FAST campaign in Qatar. The behavioral responses of the participants (total n = 165) on stroke onset included; immediately activated EMS (n = 55, 33.3%), called friends/relatives (n = 69, 41.8%), drove to hospital (n = 33, 20%), decided to rest and waited for improvement in condition (n = 21, 12.7%), and 12 (7.3%) responded as none of the above. Of the participants who admitted having watched the Act FAST campaign, 92.6% (25/27) reported that the campaign affected their response to stroke onset. There was no association of ethnicity, marital status or FAST campaign awareness with behavioral response of EMS activation on stroke onset. Conclusions The foreknowledge of stroke signs and symptoms and the Act FAST campaign was low in the community. However, seeking help by activating EMS at stroke onset was generally high in the study population irrespective of the awareness to the campaign.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".