Validation of a Spanish Version of the Information Needs in Cardiac Rehabilitation Scale to Assess Information Needs and Preferences in Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND: The identification of information needs is considered the first step to increase knowledge that ultimately could improve health outcomes in cardiac rehabilitation (CR). OBJECTIVE: The aim of this study was to psychometrically validate the Spanish Information Needs in Cardiac Rehabilitation (INCR). METHODS: The Spanish INCR was psychometrically tested in 184 patients undergoing CR. The internal consistency was assessed using Cronbach α, factor structure was assessed using exploratory factor analysis, and criterion validity regarding educational level, occupation, and duration in CR was assessed. RESULTS: Cronbach α was .97. Factor analysis revealed 10 factors, all internally consistent. Criterion validity was supported by significant differences in total INCR scores by educational level (P < .01), occupation (P < .01), and duration in CR (P < .05). Emergency/safety was the greatest information need perceived by patients. CONCLUSIONS: The Spanish INCR was demonstrated to have good reliability and validity. This tool can be applicable in clinical and research settings, assessing patients' information needs during CR and as part of education programming.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".