Compulsive Health-Related Internet Use and Cyberchondria
Bibliographic record
Abstract
BACKGROUND: Cyberchondria denotes excessive and repeated online health-related searches associated with an increase in health anxiety. Such searches persist in those with cyberchondria, despite the negative consequences, resembling a pattern of compulsive Internet use. OBJECTIVES: The aim of the present study was to assess compulsive health-related Internet use in relation to cyberchondria while controlling for related variables. METHOD: Adult participants (N = 749) were recruited from an online platform. They completed questionnaires assessing the severity of cyberchondria (via the Cyberchondria Severity Scale [CSS]), compulsive Internet use adapted for online health-related seeking (via the adapted Compulsive Internet Use Scale [CIUS]), and levels of intolerance of uncertainty and anxiety, as well as depressive, somatic, and obsessive-compulsive symptoms. A logistic regression analysis was carried out to identify predictors of scores above a cutoff value on the CIUS, indicating compulsive health-related Internet use. RESULTS: The regression output showed that only the CSS total score and sex made a unique, statistically significant contribution to the model, leading to the correct classification of 78.6% of the cases. Of the CSS subscales, compulsion and distress were the most strongly associated with compulsive health-related Internet use. CONCLUSIONS: The finding that the adapted CIUS scores are associated with cyberchondria indicates that cyberchondria has a compulsive component, at least in terms of health-related Internet use. It also suggests that compulsive health-related Internet use persists despite the distress associated with this activity. Males may engage in cyberchondria more compulsively than females. These findings have implications for research and clinical practice.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".