Factors Affecting Referral and Patient Access to Heart Function Clinics in Ontario: A Qualitative Study of Stakeholders
Bibliographic record
Abstract
Background: Though heart failure patients benefit from multidisciplinary care in heart function clinics (HFCs), utilization is suboptimal and inequitable. This study investigated factors influencing referral and patient access to HFCs from multiple stakeholders' perspectives, namely policy-makers (PM), providers at HFCs and patients. Methods: In this qualitative study, semi-structured interviews with a purposive sample of Ontario stakeholders were conducted between February-June 2020 and July-December 2022 (paused due to pandemic) via Teams. Interview transcripts were concurrently analyzed using systematic text condensation with Nvivo. Two authors coded individually, with disagreements discussed with senior author. Results: Interviews with 7 HFCs (6 physicians, 1 nurse), 6 PM and 4 patients were completed before saturation; 5 themes emerged. First, with regard to health system organization, stakeholders reported gaps related to continuity of care, limited capacity and insufficient funding. Second, with regard to referral appropriateness and timeliness, sub-themes related to unclear referral criteria, varying clinic scope, and delays in triage, testing and time-to-visit. The third theme related to clinic characteristics, raised issues of varying clinic services and composition of healthcare professions/expertise. The fourth theme regarding patient factors related to comorbidity/frailty, socioeconomic status, barriers due to location (parking, traffic) and affinity to specific providers. The final theme related to the COVID-19 pandemic concerned increased referral volumes, loss to follow-up care, transition to online delivery modalities and patient refusal of in-person visits. Many facilitators to improve HFC referral and access were raised. Conclusions: Resources must be provided, and stakeholders brought together to standardize and integrate the HF care continuum.
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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) | 0.000 | 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) | 0.000 | 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".