Translation, Cultural Adaptation, and Validation of a Korean Version of the Information Needs in Cardiac Rehabilitation Scale
Bibliographic record
Abstract
OBJECTIVE: : To translate and culturally adapt the Information Needs in Cardiac Rehabilitation (INCR) questionnaire into Korean and perform psychometric validation. METHODS: : The original English version of the INCR, in which patients are asked to rate the importance of 55 topics, was translated into Korean (INCR-K) and culturally adapted. The INCR-K was tested on 101 cardiac rehabilitation (CR) participants at Kangwon National University Hospital and Seoul National University Bundang Hospital in Korea. Structural validity was assessed using principal component analysis, and Cronbach's alpha of the areas was computed. Criterion validity was assessed by comparing information needs according to CR duration and knowledge sufficiency according to receipt of education. Half of the participants were randomly selected for 1 month of re-testing to assess their responsiveness. RESULTS: : Following cognitive debriefing, the number of items was reduced to 41 and ratings were added to assess participants' sufficient knowledge of each item. The INCR-K structure comprised eight areas, each with sufficient internal consistency (Cronbach's alpha>0.7). Criterion validity was supported by significant differences in mean INCR-K scores based on CR duration and knowledge sufficiency ratings according to receipt of education (p<0.05). Information needs and knowledge sufficiency ratings increased after 1 month of CR, thus supporting responsiveness (p<0.05). CONCLUSION: : The INCR-K demonstrated adequate face, content, cross-cultural, structural, and criterion validities, internal consistency, and responsiveness. Information needs changed with CR, such that multiple assessments of information needs may be warranted as rehabilitation progresses to facilitate patient-centered education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".