The influence of ethnicity and gender on navigating an acute coronary syndrome event
Bibliographic record
Abstract
BACKGROUND: Ethnicity and gender may influence acute coronary syndrome patients recognizing symptoms and making the decision to seek care. OBJECTIVE: To examine these potential differences in European (Caucasian), Chinese and South Asian acute coronary syndrome patients. METHODS: In-depth interviews were conducted with 20 European (Caucasian: 10 men/10 women), 18 Chinese (10 men/eight women) and 19 South Asian (10 men/nine women) participants who were purposively sampled from those participating in a large cohort study focused on acute coronary syndrome. Analysis of transcribed interviews was undertaken using constant comparative methods. RESULTS: Participants followed the process of: having symptoms; waiting/denying; justifying; disclosing/ discovering; acquiescing; taking action. The core category was 'navigating the experience'. Certain elements of this process were in the forefront, depending on participants' ethnicity and/or gender. For example, concerns regarding language barriers and being a burden to others varied by ethnicity. Women's tendency to feel responsibility to their home and family negatively impacted the timeliness in their decisions to seek care. Men tended to disclose their symptoms to receive help, whereas women often waited for their symptoms to be discovered by others. Finally, the thinking that symptoms were 'not-urgent' or something over which they had no control and concern regarding potential costs to others were more prominent for Chinese and South Asian participants. CONCLUSION: Ethnic- and gender-based differences suggest that education and support, regarding navigation of acute coronary syndrome and access to care, be specifically targeted to ethnic communities.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| 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".