Differential Impact of eHealth Literacy on Wellness Behaviors of Iranian Nurses: Descriptive Correlational Cross-Sectional Study
Bibliographic record
Abstract
Background: Nurses play a pivotal role in health care delivery and health education. However, their demanding work environments, characterized by irregular shifts and high stress, often hinder their ability to adopt healthy lifestyles, compromising both their well-being and their effectiveness as role models for health promotion. With the rise of digital health technologies, eHealth literacy-the capacity to seek, evaluate, and apply online health information-has emerged as a critical factor influencing health-promoting behaviors among health care professionals. Objective: This study aims to examine the association between eHealth literacy and healthy lifestyle behaviors among Iranian nurses, focusing on nutrition, physical activity, stress management, health responsibility, interpersonal relations, and spiritual growth. Methods: We conducted a cross-sectional descriptive-analytical study in Tehran, Iran, from November 2024 to February 2025. A total of 334 registered nurses from 7 public and teaching hospitals participated. Data were collected via the eHealth Literacy Scale and the Health-Promoting Lifestyle Profile II. Spearman correlation and multivariate linear regression analyses were performed, with statistical significance set at P<.05. Results: Of 334 nurses, 234 (70.1%) had moderate eHealth literacy, 178 (53.3%) had good healthy lifestyle scores, and none scored low. A significant positive correlation was found between eHealth literacy and overall healthy lifestyle (r=0.565; P<.001), with the strongest associations observed for spiritual growth (r=0.537), health responsibility (r=0.437), and interpersonal relationships (r=0.467). Associations with stress management (r=0.318), nutrition (r=0.321), and physical activity (r=0.289) were weaker but remained statistically substantial. Conclusions: Higher eHealth literacy is associated with healthier lifestyles, particularly in the areas of spiritual growth and health responsibility. Workplace barriers, such as rotating shifts, limit physical activity and stress management. Targeted eHealth training and wellness programs are needed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".