Translation, Cross-Cultural Adaptation and Psychometric Validation of the Arabic Version of the Cardiac Rehabilitation Barriers Scale (CRBS-A) with Strategies to Mitigate Barriers
Bibliographic record
Abstract
Cardiac rehabilitation (CR) utilization is low, particularly in Arabic-speaking countries. This study aimed to translate and psychometrically validate the CR Barriers Scale in Arabic (CRBS-A), as well as strategies to mitigate them. The CRBS was translated by two bilingual health professionals independently, followed by back-translation. Next, 19 healthcare providers, followed by 19 patients rated the face and content validity (CV) of the pre-final versions, providing input to improve cross-cultural applicability. Then, 207 patients from Saudi Arabia and Jordan completed the CRBS-A, and factor structure, internal consistency, construct, and criterion validity were assessed. Helpfulness of mitigation strategies was also assessed. For experts, item and scale CV indices were 0.8–1.0 and 0.9, respectively. For patients, item clarity and mitigation helpfulness scores were 4.5 ± 0.1 and 4.3 ± 0.1/5, respectively. Minor edits were made. For the test of structural validity, four factors were extracted: time conflicts/lack of perceived need and excuses; preference to self-manage; logistical problems; and health system issues and comorbidities. Total CRBS-A α was 0.90. Construct validity was supported by a trend for an association of total CRBS with financial insecurity regarding healthcare. Total CRBS-A scores were significantly lower in patients who were referred to CR (2.8 ± 0.6) vs. those who were not (3.6 ± 0.8), confirming criterion validity (p = 0.04). Mitigation strategies were considered very helpful (mean = 4.2 ± 0.8/5). The CRBS-A is reliable and valid. It can support identification of top barriers to CR participation at multiple levels, and then strategies for mitigating them can be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".