Older immigrant Sikh men's perspective of the challenges of managing coronary heart disease risk
Bibliographic record
Abstract
Background Gender and ethnocultural affiliation can have a significant impact on peoples’ beliefs about, and their capacity to manage, their health. We aimed to describe the gender- and ethnoculturally-based influences associated with the process that Sikh men undergo when faced with managing coronary artery disease (CAD) risk. Methods This was a grounded theory study with 10 Sikh men. Data were collected through audio-taped semi-structured interviews. The transcribed interviews were analyzed using constant comparative methods. Results The core variable was ‘meeting the challenge’. There were three main phases that encompassed the process of managing CAD and its associated risks. These included: pre-diagnosis or event, the liminal (changing) self, and living with CAD. The most salient risk factors that Sikh men reported included ongoing stress, high levels of alcohol intake and reduced physical activity. The challenges to managing these risk factors included economically-driven change in status within the family, language barriers, and religious beliefs regarding destiny. Conclusions Older immigrant Sikh men may encounter difficulty accessing the healthcare system due to language barriers and religious beliefs, and they are disinclined to investigate the causes of their illness. Health-care providers, and those that create health policy, should work with the Sikh community to develop ethnoculturally sensitive care, and to develop resources to increase health promotion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".