The association of electronic health literacy with behavioural and psychological coronary artery disease risk factors in patients after percutaneous coronary intervention: a 12-month follow-up study
Bibliographic record
Abstract
Aims: Fundamental roadblocks, such as non-use and low electronic health (eHealth) literacy, prevent the implementation of eHealth resources. The aims were to study internet usage for health information and eHealth literacy in patients after percutaneous coronary intervention (PCI). Further, we aimed to evaluate temporal changes and determine whether the use of the internet to find health information and eHealth literacy were associated with coronary artery disease (CAD) risk factors at the index admission and 12-month follow-up of the same population. Methods and results: , the eHealth literacy scale, and assessment of clinical, behavioural, and psychological CAD risk factors. Regression analyses were used. Patients' use of the internet for health information and their eHealth literacy were moderate at baseline but significantly lower at 12-month follow-up. Non-users of the internet for health information were more often smokers and had a lower burden of anxiety symptoms. Lower eHealth literacy was associated with a higher burden of depression symptoms at baseline and lower physical activity and being a smoker at baseline and at 12-month follow-up. Conclusion: Non-use of the internet and lower eHealth literacy need to be considered when implementing eHealth resources, as they are associated with behavioural and psychological CAD risk factors. eHealth should therefore be designed and implemented with high-risk CAD patients in mind. Clinical trial registration: ClinicalTrials.gov NCT03810612 https://clinicaltrials.gov/ct2/show/NCT03810612.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".