Psychometric validation of the Cardiac Rehabilitation Barriers Scale
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the factor structure and psychometric properties of the Cardiac Rehabilitation Barriers Scale (CRBS). DESIGN, SETTING, AND PARTICIPANTS: In total, 2636 cardiac inpatients from 11 hospitals completed a survey. One year later, participants completed a follow-up survey, which included the CRBS. A subsample of patients also completed a third survey which included the CRBS, the Cardiac Rehabilitation Enrolment Obstacles scale, and the Beliefs About Cardiac Rehabilitation scale three weeks later. The CRBS asked participants to rate 21 cardiac rehabilitation barriers on a five-point Likert scale regardless of cardiac rehabilitation referral or enrolment. RESULTS: Maximum likelihood factor analysis with oblique rotation resulted in a four-factor solution: perceived need/healthcare factors (eigenvalue = 6.13, Cronbach's α = .89), logistical factors (eigenvalue = 5.83, Cronbach's α = .88), work/time conflicts (eigenvalue = 3.78, Cronbach's α = .71), and comorbidities/functional status (eigenvalue = 4.85, Cronbach's α = .83). Mean total perceived barriers were significantly greater among non-enrollees than cardiac rehabilitation enrollees (P < .001). Convergent validity with the Beliefs About Cardiac Rehabilitation and Cardiac Rehabilitation Enrolment Obstacles scales was also demonstrated. Test-retest reliability of the CRBS was acceptable (intraclass correlation coefficient = .64). CONCLUSION: The CRBS consists of four subscales and has sound psychometric properties. The extent to which identified barriers can be addressed to facilitate greater cardiac rehabilitation utilization warrants future study.
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 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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".