Nurse practitioners: improving coronary heart disease management in South Asian Canadians
Bibliographic record
Abstract
Coronary heart disease rates are much higher amongst South Asian Canadians than any other Canadian ethnic group. This project asks the question, How will a nurse practitioner implement culturally sensitive coronary heart disease interventions to improve the health outcomes of the South Asian population? To respond to this research question, a literature review and analysis of studies describing various research data, as well as perceptions among the South Asian community, was completed. Evidence suggests that the South Asian population has a genetic tendency to coronary heart disease. More importantly, there are environmental, dietary, and lifestyle factors that might also contribute to this higher incidence. South Asians are underutilizing the traditional health service. Research indicates that health services and treatments are not culturally relevant. Madeline Leininger's theoretical framework, which advocates for the theory of Culture Care Diversity and Universality, is utilized to examine the literature and used to prepare culturally sensitive coronary heart disease interventions for the South Asian population. In this paper, it is argued that implementation of culturally sensitive coronary heart disease interventions delivered by a nurse practitioner can positively influence the management and the outcome of coronary heart disease in the South Asian population. --P. ii.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".