Cardiac rehabilitation programs: An investigation into the reasons for non-attendance in Qatar
Bibliographic record
Abstract
Despite the many benefits of cardiac rehabilitation programs, participation of eligible patients in these programs can be low. Understanding the factors that prevent cardiac patients from attending these programs can provide healthcare professionals with insights on how to reduce the barriers and increase participation. The purpose of this quantitative descriptive study was to investigate the reasons why patients residing in Qatar do not attend cardiac rehabilitation (Phase 11). The target population were 850 patients who were referred to cardiac rehabilitation during an eight-month period in 2015, but who did not attend Phase 11 of the program. Individuals were invited to complete a phone survey which included socio-demographic questions and a pre-existing instrument called the Cardiac Rehabilitation Barriers Scale. Forty-six participants completed the phone survey in 2016. The most frequently reported barriers were: work responsibilities (56.5%), time constraints (50%), already exercising at home/community (39.1%), distance to program (39.1%), travel out of country (32.6%), and not needing cardiac rehabilitation (32.6%). Results of this study can be used to inform the development of new policies that will reduce the barriers and promote attendance. Future qualitative research can be done to gain deeper insights into the reasons for non-attendance.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".