Referral and access to heart function clinics: A realist review
Bibliographic record
Abstract
RATIONALE, AIM, AND OBJECTIVES: Heart failure (HF) clinics are highly effective, yet not optimally utilized. A realist review was performed to identify contexts (eg, health system characteristics, clinic capacity, and siting) and underlying mechanisms (eg, referring provider knowledge of clinics and referral criteria, barriers in disadvantaged patients) that influence utilization (provider referral [ie, of all appropriate and no inappropriate patients] and access [ie, patient attends ≥1 visit]) of HF clinics. METHODS: Following an initial scoping search and field observation in a HF clinic, we developed an initial program theory in conjunction with our expert panel, which included patient partners. Then, a literature search of seven databases was searched from inception to December 2019, including Medline; Grey literature was also searched. Studies of any design or editorials were included; studies regarding access to cardiac rehabilitation, or a single specialist for example, were excluded. Two independent reviewers screened the abstracts, and then full-texts. Relevant data from included articles were used to refine the program theory. RESULTS: A total of 29 papers from five countries (three regions) were included. There was limited information to support or refute many elements of our initial program theory (eg, referring provider knowledge/beliefs, clinic inclusion/exclusion criteria), but refinements were made (eg, specialized care provided in each clinic, lack of patient encouragement). Lack of capacity, geography, and funding arrangements were identified as contextual factors, explaining a range of mechanistic processes, including patient clinical characteristics and social determinants of health as well as clinic characteristics that help to explain inappropriate and low use of HF clinics (outcome). CONCLUSION: Given the burden of HF and benefit of HF clinics, more research is needed to understand, and hence overcome sub-optimal use of HF clinics. In particular, an understanding from the perspective of referring providers is needed.
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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 | 0.035 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 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".