Exploring the impact of financial barriers on secondary prevention of heart disease
Bibliographic record
Abstract
BACKGROUND: Patients with coronary artery disease experience various barriers which impact their ability to optimally manage their condition. Financial barriers may result in cost related non-adherence to medical therapies and recommendations, impacting patient health outcomes. Patient experiences regarding financial barriers remain poorly understood. Therefore, we used qualitative methods to explore the experience of financial barriers to care among patients with heart disease. METHODS: We conducted a qualitative descriptive study of participants in Alberta, Canada with heart disease (n = 13) who perceived financial barriers to care. We collected data using semi-structured face-to-face or telephone interviews inquiring about patients experience of financial barriers and the strategies used to cope with such barriers. Multiple analysts performed inductive thematic analysis and findings were bolstered by member checking. RESULTS: The aspects of care to which participants perceived financial barriers included access to: medications, cardiac rehabilitation and exercise, psychological support, transportation and parking. Some participants demonstrated the ability to successfully self-advocate in order to effectively navigate within the healthcare and social service systems. CONCLUSION: Financial barriers impacted patients' ability to self-manage their cardiovascular disease. Financial barriers contributed to non-adherence to essential medical therapies and health recommendations, which may lead to adverse patient outcomes. Given that it is such a key skill, enhancing patients' self-advocacy and navigation skills may assist in improving patient health outcomes.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".