What is the role of masculinity in white and South Asian men's decisions to seek medical help for cardiac chest pain?
Bibliographic record
Abstract
OBJECTIVES: To explore how masculinity influences white, Indian and Pakistani men's interpretation of cardiac chest pain and the decision to seek or delay seeking medical help. METHOD: Semi-structured interviews were conducted with 36 men of white ethnicity (UK ancestry) and 20 men of South Asian ethnicity (Indian or Pakistani ancestry) who had a confirmed diagnosis of acute myocardial infarction or angina pectoris. The study setting was two coronary care units and eight cardiology wards situated in two teaching hospitals in the North of England. All interviews were tape-recorded, transcribed verbatim and analysed using a constant comparative method. RESULTS: Displaying a high threshold for pain and discomfort was a masculine attribute valued by white men in the study but not by the Indian and Pakistani men. White men's fears of being seen to be weak contributed to delays in seeking medical treatment and led to reluctance to disclose symptoms to others. Indian and Pakistani men emphasized wisdom, education and responsibility for the family and their own health as more valued masculine attributes, and this contributed to a greater willingness to seek medical help. CONCLUSION: Masculinity had a differing role in white and South Asian men's decisions to seek or delay seeking medical help for chest pain. The study supports the development of coronary heart disease policy and practice that acknowledges men's gender as an important determinant of their health behaviour, but illustrates that there is no probable single appropriate approach to tackling the delay in how men seek treatment for chest pain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".