Cardiac Specialists’ Perspectives on Barriers to Cardiac Rehabilitation Referral and Participation in a Low-Resource Setting
Bibliographic record
Abstract
BACKGROUND: Cardiac specialists are arguably the most influential providers in ensuring patients access cardiac rehabilitation (CR). Physician barriers to referral have been scantly investigated outside of high-income settings, and not qualitatively. AIM: This study investigated cardiac specialists' perceptions of barriers and facilitators to patient CR participation in a low-resource setting, with a focus on referral. METHODS: In this qualitative study, focus groups were conducted with conventional content analysis. Thirteen of 14 eligible cardiac specialists working in Yazd, Iran, participated in 1 or both focus groups (n = 9 and n = 10, respectively). The recording of the first focus group was transcribed into a word file verbatim, and the accuracy of the content of all field notes and the transcripts was approved by the research team, which was then analyzed inductively. Following a similar process, saturation was achieved with the second focus group. RESULTS: Four themes emerged: "physician factors," "center factors," "patient factors," and "cultural factors." Regarding "physician factors," most participants mentioned shortage of time. Regarding "center factors," most participants mentioned poor physician-patient-center coordination. In "patient factors," the subcategories that arose were socioeconomic challenges and clinical condition of the patients. "Cultural factors" related to lack of belief in behavioral/preventive medicine. CONCLUSIONS: Barriers to CR referral and participation were multilevel, as in high-resource settings. However, relative recency of the introduction of CR in these settings seemed to cause great lack of awareness. Cultural beliefs may differ, and communication from CR programs to referring providers was a particular challenge in this setting.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".