Spanish Translation, Adaptation, and Validation of Strategies to Mitigate Cardiac Rehabilitation Barriers
Bibliographic record
Abstract
Background: Despite its demonstrated benefits, cardiac rehabilitation (CR) is grossly underutilized. Validated interventions are needed to mitigate the established barriers to accessing and adhering to these services. A strategy has been created based on the Cardiac Rehabilitation Barriers Scale (CBRS), by the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR), with initial support of helpfulness. Purpose: To translate, adapt, and content validate the CR barrier mitigation strategy for the Colombian context. Methods: A 2-phase exploratory sequential multimethod study was conducted in accordance with best practices. The 21 barrier mitigation strategies were first translated to Spanish by a bilingual health care professional and then adapted for the Colombian population following semi-structured interviews with 6 Colombian CR staff. The second phase of content validation involved 8 experts and 8 CR patients, who evaluated the mitigation strategies regarding engagement, comprehensibility, applicability, acceptability, and motivation on a 5-point Likert scale (higher scores more favorable). The content validity coefficient was computed. Results: Content analysis of interviews resulted in revisions to all 21 Spanish barrier mitigation strategies. Content validity of the revised version was rated as at least acceptable (≥0.70) for all 21 strategies, but each strategy was rated as requiring revision on 1 to 4 of the above indicators. Revisions ensued accordingly. Conclusions: The rigorous adaptation and content validation process has established the Spanish CR barrier mitigation strategies, which will now be tested at the bedside in a randomized controlled trial in Colombia to determine effect on CR use.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".