South Asians’ cardiac medication adherence
Bibliographic record
Abstract
AIM: This paper is a report of a narrative review examining the current state of knowledge regarding adherence with cardiac medication among South Asian cardiac patients. BACKGROUND: South Asians experience higher rates of cardiovascular disease than any other ethnic group. South Asians may be less adherent with a cardiac medication regimen than Caucasians. The factors contributing to adherence are important to discover to assist South Asians to optimize their cardiac health. DATA SOURCES: CINAHL, Medline (Ovid), PsychINFO, EMB Reviews-(Cochrane), and EMBASE were accessed using the key words: 'South Asian', 'Asia', 'East India', 'India', 'Pakistan', 'Bangladesh', 'Sri Lanka', 'medication compliance', 'medication noncompliance' and 'medication adherence'. English language papers published from January 1980 to January 2013 were eligible for inclusion. REVIEW METHODS: Abstracts were reviewed for redundancy and eligibility by the primary author. Manuscripts were then retrieved and reviewed for eligibility and validity by the first and last authors. Content analysis strategies were used for the synthesis. RESULTS: Thirteen papers were in the final data set; most were conducted in India and Pakistan. Medication side-effects, cost, forgetfulness and higher frequency of dosing contributed to non-adherence. South Asian immigrants also faced language barriers, which contributed to non-adherence. Knowledge regarding the medications prescribed was a factor that increased adherence. CONCLUSION: South Asians' non-adherence to cardiac medications is multifaceted. How South Asians who newly immigrate to Western countries make decisions regarding their cardiac medication adherence ought to be explored in greater detail.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".