Bibliographic record
Abstract
Background: The use of evidence-based medications for the management of cardiovascular disease (CVD) is low worldwide. A key strategy to improving use of medications is to understand the barriers to their use. This thesis aims to identify barriers that may influence use of these medications in high, middle, and low income countries. Data on barriers in low and middle income countries are especially lacking. We postulate that in those settings lack of availability and affordability of proven medications are key barriers to medication use. \nMethods: We initially systematically reviewed the literature on barriers to medication use. Since data on these barriers for the management of CVD are sparse, the review included studies focused on hypertension, because it is the leading risk factor for CVD. Baseline data from the PURE study were then used to investigate whether availability and affordability of medications influence their use for secondary prevention of CVD. PURE is a prospective study that recruited adults between the ages of 35 to 70 years from 17 high, middle, and low income countries. Availability and affordability of medications were documented for each country income group, and the associations between these two potential barriers and medication use was explored after accounting for other factors that may influence medication use. \nResults: The review showed that in high income countries, non-healthcare system related factors, such as lack of knowledge and motivation, were more commonly reported as barriers, whereas in low and middle income countries healthcare system factors were most commonly reported as barriers to hypertension management. However, very few studies were conducted in low and middle income countries and so there is limited information on whether availability and affordability of medications affect their use. Results from the PURE study indicate that medications recommended for the secondary prevention of CVD were often not available and when available, they were not affordable for a high proportion of individuals in low and middle income countries. Lack of availability and low affordability were strongly associated with medication use in these settings. \nConclusions: Barriers to medication use are context specific and interventions to improve use should be tailored to barriers depending on the setting. In high income countries where the medications are usually available and affordable interventions should target knowledge and motivation barriers. In low and middle income countries, the focus should be on healthcare system interventions to improve the availability and affordability of medications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".