Self-reported financial barriers to care among patients with cardiovascular-related chronic conditions.
Bibliographic record
Abstract
BACKGROUND: People with chronic conditions who do not achieve therapeutic targets have a higher risk of adverse health outcomes. Failure to meet these targets may be due to a variety of barriers. This article examines self-reported financial barriers to health care among people with cardiovascular-related chronic conditions. DATA AND METHODS: A population-based survey was administered to western Canadians with cardiovascular-related chronic conditions (n = 1,849). Associations between self-reported financial barriers and statin use, the likelihood of stopping use of prescribed medications, and emergency department visits or hospitalizations were assessed. RESULTS: More than 10% respondents reported general financial barriers (12%) and lack of drug insurance (14%); 4% reported financial barriers to accessing medications. Emergency department visits or hospitalizations were 70% more likely among those reporting a general financial barrier. Those reporting a financial barrier to medications were 50% less likely to take statins and three times more likely to stop using prescribed medications. Individuals without drug insurance were nearly 30% less likely to take statins. INTERPRETATION: In this population, self-reported financial barriers were associated with lower medication use and increased likelihood of emergency department visits or hospitalization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".