Understanding Roadblocks to Heart Failure Care ( <scp>UNLOCK</scp> ‐ <scp>HF</scp> ): A Convergent Parallel Mixed‐Methods Study in Kerala
Bibliographic record
Abstract
OBJECTIVE: Heart failure (HF) causes substantial morbidity, premature mortality and escalating healthcare costs, with prevalence rising fastest in low- and middle-income countries. Although guideline-recommended care can reduce mortality, its uptake remains suboptimal. We aimed to explore the roadblocks to optimal HF care in Kerala, a state in India with a high cardiovascular disease burden. METHODS: We conducted a convergent parallel mixed-methods study. We collected availability and price data of guideline-recommended HF medicines from 30 pharmacies (government-subsidised 6; private-retail 24) and diagnostics and interventional procedures from 4 private hospitals. Interviews (10 HF patients, 7 carers, 4 physicians and 4 policymakers) explored roadblocks to prevention, diagnosis and treatment. RESULTS: Mean availability of HF medicines was 45% in government-subsidised pharmacies and 66% in private pharmacies. Mean availability of HF diagnostics and interventional procedures was 89% and 57% in private hospitals. The lowest paid worker in Kerala would spend on average 0.3 days' wages to purchase a monthly supply of HF medicines in the government-subsidised pharmacies, and all but two HF medicines were affordable. The same worker would on average spend 1.4 days' wages for medicines in the private-retail pharmacies, 0.9 days' wages for cardiologist consultations, 8.4 days' wages for diagnostics and 1387 days' wages for interventional procedures. Interviews revealed care fragmentation, limited integration of HF management within broader programs, and gaps in patient and provider awareness. CONCLUSIONS: We identified several roadblocks to optimal HF care at various levels of healthcare, mainly low availability, poor affordability and fragmented care. Addressing these roadblocks requires a multilevel coordinated effort among all health system actors to ensure equitable and effective HF care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".