Public Knowledge and Attitude Regarding Symptoms of Acute Coronary Syndrome and Its Related Risk Factors in Western Region, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndrome (ACS) is the leading cause of mortality and morbidity worldwide. Recognition of its symptoms affects the time-sensitive benefits of reperfusion therapy. Furthermore, lowering the risk factors can prevent long-term complications. OBJECTIVES: To evaluate the public knowledge and perception of the symptoms and risk factors of ACS in the Saudi-Western region. METHOD: A cross-sectional study was conducted on a convenience sample of 733 among the general population in the western region of Saudi Arabia by using the Acute Coronary Syndrome Response Index, with additional questions about risk factors for heart attack and physical activities. The research information was acquired through a self-administered questionnaire without any identifying personal information. RESULT: Participants demonstrated awareness of certain ACS symptoms and risk factors. Chest pain was widely recognized (49.2%, n = 361), followed by shortness of breath (44.8%, n = 329), arm pain or shoulder pain (38.6%, n = 283), palpitation (37.3%, n = 274), and fatigue (22.2%, n = 163). A total of 544 (74.2%) and 474 (64.6%) respondents were aware that smoking and obesity are the most common risk factors for ACS, respectively. However, gaps persisted, particularly regarding the association between diabetes mellitus and ACS, with 31.6% (n = 232) reporting diabetes mellitus. A total of 331 (45.2%) and 322 (43.9%) study sample were unsure whether they could identify ACS in themselves or other people. However, 391 (53.3%) decided that they should go to the hospital as soon as possible when they have chest pain that does not stop after 15 minutes. Notably, female participants demonstrated substantially higher knowledge (OR = 2.40, p = 0.001). The study highlights the influence of gender, age, and education on ACS-related awareness. CONCLUSION: This study provides valuable insights into ACS awareness in the western region of Saudi Arabia. Relatively older respondents, female participants, and those with postgraduate education were more knowledgeable about ACS than the others. These findings emphasize the importance of tailored interventions for specific demographic groups in enhancing public health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".