Motivators and Deterrents to Diet Change in Low Socio-Economic Pakistani Patients With Cardiovascular Disease
Bibliographic record
Abstract
This study explores factors that affect the people of low socioeconomic status regarding food choices after diagnosis with cardiovascular disease. Qualitative approach was used to identify the important factors associated with dietary changes as a result of their disease. Twenty-four participants were interviewed from two cardiac facilities in Karachi, the largest metropolitan city of Pakistan. Data were analyzed to identify the themes using the interpretative description approach. While most participants understood the need for dietary changes, few were able to follow recommended diets. Their food choices were primarily influenced by financial constraints as well as cultural, familial, and religious values and practices. The challenge for health care providers lies in understanding the economical, sociocultural, and religious factors that influence behavioral changes which, in turn, affect dietary choices. It is apparent that cardiovascular risk and disease outcomes for the people of low socioeconomic status are likely to escalate. Thus, it is necessary to address the sociocultural, religious, and behavioral factors affecting dietary choices. Achieving this imperative requires an intersectorial, multilevel intervention for the prevention of cardiovascular diseases in people of low socioeconomic status.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".