Psychometric Properties of the Norwegian Version of the Electronic Health Literacy Scale (eHEALS) Among Patients After Percutaneous Coronary Intervention: Cross-Sectional Validation Study
Bibliographic record
Abstract
BACKGROUND: Web-based technology has recently become an important source for sharing health information with patients after an acute cardiac event. Therefore, consideration of patients' perceived electronic health (eHealth) literacy skills is crucial for improving the delivery of patient-centered health information. OBJECTIVE: The aim of this study was to translate and adapt the eHealth Literacy Scale (eHEALS) to conditions in Norway, and to determine its psychometric properties. More specifically, we set out to determine the reliability (internal consistency, test-retest) and construct validity (structural validity, hypotheses testing, and cross-cultural validity) of the eHEALS in self-report format administered to patients after percutaneous coronary intervention. METHODS: The original English version of the eHEALS was translated into Norwegian following a widely used cross-cultural adaptation process. Internal consistency was calculated using Cronbach α. The intraclass correlation coefficient (ICC) was used to assess the test-retest reliability. Confirmatory factor analysis (CFA) was performed for a priori-specified 1-, 2-, and 3-factor models. Demographic, health-related internet use, health literacy, and health status information was collected to examine correlations with eHEALS scores. RESULTS: A total of 1695 patients after percutaneous coronary intervention were included in the validation analysis. The mean age was 66 years, and the majority of patients were men (1313, 77.46%). Cronbach α for the eHEALS was >.99. The corresponding Cronbach α for the 2-week retest was .94. The test-retest ICC for eHEALS was 0.605 (95% CI 0.419-0.743, P<.001). The CFA showed a modest model fit for the 1- and 2-factor models (root mean square error of approximation>0.06). After modifications in the 3-factor model, all of the goodness-of-fit indices indicated a good fit. There was a weak correlation with age (r=-0.206). Between-groups analysis of variance showed a difference according to educational groups and the eHEALS score, with a mean difference ranging from 2.24 (P=.002) to 4.61 (P<.001), and a higher eHEALS score was found for patients who were employed compared to those who were retired (mean difference 2.31, P<.001). The eHEALS score was also higher among patients who reported using the internet to find health information (95% CI -21.40 to -17.21, P<.001), and there was a moderate correlation with the patients' perceived usefulness (r=0.587) and importance (r=0.574) of using the internet for health information. There were also moderate correlations identified between the eHEALS score and the health literacy domains appraisal of health information (r=0.380) and ability to find good health information (r=0.561). Weak correlations with the mental health composite score (r=0.116) and physical health composite score (r=0.116) were identified. CONCLUSIONS: This study provides new information on the psychometric properties of the eHEALS for patients after percutaneous coronary intervention, suggesting a multidimensional rather than unidimensional construct. However, the study also indicated a redundancy of items, indicating the need for further validation studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT03810612; https://clinicaltrials.gov/ct2/show/NCT03810612.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 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".