Factors Affecting Healthcare Provider Referral to Heart Function Clinics
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) care providers are gatekeepers for patients to appropriately access lifesaving HF clinics. OBJECTIVE: The aim of this study was to investigate referring providers' perceptions regarding referral to HF clinics, including the impact of provider specialty and the coronavirus disease pandemic. METHODS: An exploratory, sequential design was used in this mixed-methods study. For the qualitative stage, semistructured interviews were performed with a purposive sample of HF providers eligible to refer (ie, nurse practitioners, cardiologists, internists, primary care and emergency medicine physicians) in Ontario. Interviews were conducted via Microsoft Teams. Transcripts were analyzed concurrently by 2 researchers independently using NVivo, using a deductive-thematic approach. Then, a cross-sectional survey of similar providers across Canada was undertaken via REDCap (Research Electronic Data Capture), using an adapted version of the Provider Attitudes toward Cardiac Rehabilitation and Referral scale. RESULTS: Saturation was achieved upon interviewing 7 providers. Four themes arose: knowledge about clinics and their characteristics, providers' clinical expertise, communication and relationship with their patients, and clinic referral process and care continuity. Seventy-three providers completed the survey. The major negative factors affecting referral were skepticism regarding clinic benefit (4.1 ± 0.9/5), a bad patient experience and believing they are better equipped to manage the patient (both 3.9). Cardiologists more strongly endorsed clarity of referral criteria, referral as normative and within-practice referral supports as supporting appropriate referral versus other professionals ( P s < .02), among other differences. One-third (n = 13) reported the pandemic impacted their referral practices (eg, limits to in-person care, patient concerns). CONCLUSION: Although there are some legitimate barriers to appropriate clinic referral, greater provider education and support could facilitate optimal patient access.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".