Multi-Method Study On Referral And Access To Heart Function Clinics
Bibliographic record
Abstract
Patients with heart failure (HF) experience significant benefits from receiving comprehensive outpatient care in specialized heart failure clinics (HF clinics). These clinics have demonstrated their effectiveness in reducing frequent HF-related hospital readmissions while maintaining cost-efficiency. Unfortunately, despite established guidelines recommending the referral of HF patients to these clinics, there exists a notable discrepancy in both access and utilization of this specialized care, creating issues of low and inequitable service utilization. The underlying reasons are largely unknown and under-researched. Therefore, this doctoral dissertation aimed to advance a scholarly understanding of factors influencing the referral decisions and access to HF clinics through a multi-method study. For this purpose, three inter-linked research studies were undertaken. Firstly, qualitative interviews were conducted with key stakeholders in HF care, including policymakers, clinic providers, and patients. This initial phase established a foundational understanding of the barriers preventing optimal access to HF clinic services. Secondly, recognizing that referring providers play a pivotal role in determining patient access to HF clinics, a mixed-method design was employed, using a sequential exploratory approach to delve into their perspectives on the challenges associated with referring patients to HF clinics. Finally, a cross-sectional survey approach was adopted to compare clinic perceptions of ideal referral criteria with those of referring providers. By identifying areas of agreement between both parties, strategies for consistent application were proposed. This dissertation contributes valuable insights for HF clinics and the broader HF community. The knowledge generated has the potential, when translated into practice, to facilitate appropriate patient access to essential HF services. The findings offer guidance to policymakers, healthcare providers, and HF patients, aiming to optimize the utilization of HF clinic services, enhance the quality of care provided, and improve overall patient 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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".