eHealth Literacy and Cyberchondria Severity Among Undergraduate Students: Mixed Methods Study
Bibliographic record
Abstract
Background: With the development of the internet, health care websites have become increasingly important by enabling easy access to health information, thereby influencing the attitudes and behaviors of individuals toward health issues. However, few studies have addressed public access to health information and self-diagnosis. Objective: This study investigated the background factors and status of cyberchondria severity among college students by conducting a nationwide sample survey using the Cyberchondria Severity Scale. Further, we explored the perspective of eHealth literacy of those with scores higher than 1 SD from the mean by analyzing their recent experiences using web-based health information. Methods: A nationally representative sample of college students was surveyed, and 802 valid responses were obtained (male: 435/802, 54.2%; female: 367/802, 45.8%; mean age 20.3, SD 1.4 years). The Cyberchondria Severity Scale was used, which consisted of 4 dimensions (increased anxiety, obsessive-compulsive hypochondria, perceived controllability, and web-based physician-patient interaction). Additionally, we recruited 9 volunteers who scored more than 1 SD above the mean for in-depth interviews on their web-based health information-seeking behaviors. Results: Significant differences were found across the 4 dimensions of cyberchondria severity (F3,2403=256.26; P<.001), with perceived controllability scoring the highest (mean 2.75, SD 0.87) and obsessive-compulsive hypochondria scoring the lowest (mean 2.19, SD 0.77). Positive correlations were observed between perceived controllability, web-based physician-patient interactions, increased anxiety, and obsessive-compulsive hypochondria (r=0.46-0.75, P<.001). Regression analysis indicated that health concern significantly predicted perceived controllability (β coefficient=0.12; P<.05) and web-based physician-patient interaction (β coefficient=0.16; P<.001). Interview data revealed that students often experienced heightened anxiety (8/9, 89%) and stress (7/9, 78%) after exposure to web-based health information, highlighting the need for improved health literacy and reliable information sources. Conclusions: The study identified both benefits and risks in college students' use of web-based health information, emphasizing the importance of critical consciousness and eHealth literacy. Future research should examine how college students move from self-awareness to actionable change and the development of critical health literacy, which are essential for effective digital health engagement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".