Compulsive Health-Related Internet Use and Cyberchondria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it