Examining Facilitators and Barriers to Cardiac Rehabilitation Adherence in a Low-Resource Setting in Latin America from Multiple Perspectives
Bibliographic record
Abstract
Cardiac rehabilitation (CR) is under-used, particularly in low-resource settings. There are few studies of barriers and facilitators to CR adherence in these settings, particularly considering multiple perspectives. In this multiple-method study, a cross-sectional survey including the Cardiac Rehabilitation Barriers Scale (each item scored on a five-point Likert scale) was administered to patients treated between February and July, 2019, in three CR centers in Colombia. A random subsample of 50 participants was invited to a focus group, along with an accompanying relative. Physiotherapists from the programs were invited to an interview, with a similar interview guide. Audio-recordings were transcribed and analyzed using interpretive description. A total of 210 patients completed the survey, and 9 patients, together with 3 of their relatives and 3 physiotherapists, were interviewed. The greatest barriers identified were costs (mean = 2.8 ± 1.6), distance (2.6 ± 1.6) and transportation (2.5 ± 1.6); the logistical subscale was highest. Six themes were identified, pertaining to well-being, life roles, weather, financial factors, healthcare professionals and health system factors. The main facilitators were encouragement from physiotherapists, relatives and other patients. The development of hybrid programs where patients transition from supervised to unsupervised sessions when appropriate should be considered, if health insurers were to reimburse them. Programs should consider the implications regarding policies of family inclusion.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".