Protocol for a qualitative study on promoting dietary change and positive food choices for poor people with low income who experience cardiovascular disease in Pakistan
Bibliographic record
Abstract
INTRODUCTION: There is a misconception that cardiovascular disease (CVD) is the burden of wealthy nations, but, in fact, it is the leading cause of death and disability-adjusted life worldwide. Healthy diets are an essential factor in the prevention of CVD. However, promoting healthy diet is challenging, particularly for people with low-socioeconomic status (SES), because poverty is linked with many risk behaviours such as smoking, unhealthy eating and obesity. Multiple factors, cultural values and beliefs interact and make healthy eating very challenging. The effects of these factors in the context of low-SES populations with CVD are largely unknown. To address this gap, this study will examine the factors that affect decisions about consuming healthy diet in Pakistanis with low SES who suffer from CVD. METHODS AND ANALYSIS: A qualitative method of interpretive description will be used. 25 participants will be selected from two cardiac rehabilitation (CR) centres in Karachi, Pakistan. Face-to-face interviews using a critical realist framework will be used to understand individual and contextual factors in the food choices of people with low SES and CVD. ATLAS.ti qualitative data analysis software will be used to identify themes and patterns in the interview data. ETHICS AND DISCUSSION: Ethical approvals were received from the Ethics Review board of University of Alberta, Canada and Aga Khan University, Karachi Pakistan. The findings will generate new knowledge about which and how factors influence the food choices of Pakistanis with CVD and low SES to provide an insight into the development of an operational framework for designing interventions for prevention of CVD. For knowledge-translation purposes, we will publish the findings in highly accessed, peer-reviewed scientific and health policy journals at the national and international level. This research protocol received IRDC (International Development Research Centre) doctoral award from International Development Research Centre, Ottawa, Canada.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".