Barriers to cardiac rehabilitation delivery in a low-resource setting from the perspective of healthcare administrators, rehabilitation providers, and cardiac patients
Bibliographic record
Abstract
BACKGROUND: Despite clinical practice guideline recommendations that cardiovascular disease patients participate, cardiac rehabilitation (CR) programs are highly unavailable and underutilized. This is particularly true in low-resource settings, where the epidemic is at its' worst. The reasons are complex, and include health system, program and patient-level barriers. This is the first study to assess barriers at all these levels concurrently, and to do so in a low-resource setting. METHODS: In this cross-sectional study, data from three cohorts (healthcare administrators, CR coordinators and patients) were triangulated. Healthcare administrators from all institutions offering cardiac services, and providers from all CR programs in public and private institutions of Minas Gerais state, Brazil were invited to complete a questionnaire. Patients from a random subsample of 12 outpatient cardiac clinics and 11 CR programs in these institutions completed the CR Barriers Scale. RESULTS: Thirty-two (35.2%) healthcare administrators, 16 (28.6%) CR providers and 805 cardiac patients (305 [37.9%] attending CR) consented to participate. Administrators recognized the importance of CR, but also the lack of resources to deliver it; CR providers noted referral is lacking. Patients who were not enrolled in CR reported significantly greater barriers related to comorbidities/functional status, perceived need, personal/family issues and access than enrollees, and enrollees reported travel/work conflicts as greater barriers than non-enrollees (all p < 0.01). CONCLUSIONS: The inter-relationship among barriers at each level is evident; without resources to offer more programs, there are no programs to which physicians can refer (and hence inform and encourage patients to attend), and patients will continue to have barriers related to distance, cost and transport. Advocacy for services 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".