Myocardial infarction symptom recognition by the lay public: the role of gender and ethnicity
Bibliographic record
Abstract
STUDY OBJECTIVE: To find out if gender and ethnicity are associated with acute myocardial infarction (AMI) symptom recognition and the recommendation of enlisting emergency medical services. DESIGN: In an experiment, a random sample of the public was provided a scenario of a person experiencing symptoms of AMI; the gender of the character (male, female, or indeterminate) was manipulated. SETTING: Vancouver, Canada PARTICIPANTS: 976 people from a population based random sample of 3419 people, 40 years of age and older, participated in a telephone survey given in English, Cantonese, Mandarin, and Punjabi. MAIN RESULTS: 78% of the respondents identified the symptoms as heart related. Unadjusted analyses showed that ethnicity, education, income, and AMI knowledge were significantly associated with symptom recognition (Chinese respondents were least likely to identify the symptoms as heart related). Thirty seven per cent recommended calling emergency services, which was associated with symptom recognition, ethnicity (Chinese respondents were least likely to make the recommendation), AMI knowledge, having an immediate family member with AMI, and having talked with a health professional about the signs and symptoms of AMI. Neither the gender of the respondent nor of the affected person in the scenario was associated with symptom recognition. CONCLUSIONS: Heart health education must be targeted to and tailored for ethnic communities. Health professionals must discuss the signs and symptoms of AMI, and the correct course of action, with their patients.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".