Can people apply ‘FAST’ when it really matters? A qualitative study guided by the common sense self-regulation model
Bibliographic record
Abstract
BACKGROUND: Early identification of stroke symptoms and rapid access to the emergency services increases an individual's chance of receiving thrombolytic therapy and reduces the likelihood of infirmity. The UK's national stroke campaign 'Act FAST' was developed to increase public awareness of stroke symptoms and highlighted the importance of rapid response by contacting emergency services. No study to date has assessed if and how people who experienced or witnessed stroke in line with the campaigns' symptoms of the FAST acronym (i.e., facial weakness, arm weakness, slurred speech, and time) may use this FAST in their response. METHODS: Semi-structured interviews with 13 stroke patients and witnesses were conducted. Interviews were theory-guided based on the Common Sense Self-Regulation Model, to understand the appraisal process of the onset of stroke symptoms and how this impacted on participants' ability to apply their knowledge of the FAST campaign. RESULTS: The majority of patients (n = 8/13) failed to correctly identify stroke and reported no impact of the campaign on their stroke recognition and response. Inability to identify stroke, perceiving symptoms to lack severity and lack of control contributed to a delay in seeking medical attention. CONCLUSION: Stroke witnesses and patients predominantly fail to identify stroke which suggest a lack of FAST application when it matters. Inaccurate risk perceptions and lack of physical control both play central roles in influencing the formation of illness representation not associated with an appropriate emergency response.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".