Age, COVID-19-related fear, insomnia symptoms and cyberchondria: a mediation model
Bibliographic record
Abstract
Introduction:The subject of our study was the role of age, fear of COVID-19 infection and insomnia as predictors of cyberchondria in a Polish sample.We were also interested in whether insomnia mediated the relationship between fear of COVID-19 infection and cyberchondria in the entire sample. Material and methods:The study sample consisted of 504 people, including 420 women and 84 men, aged 18 to 76 years (M ±SD 30.49±10.28), who were recruited through an online platform.Cyberchondria was assessed using the Polish version of the Cyberchondria Severity Scale.An 11-point numerical rating scale was used to measure the intensity of fear of COVID-19 infection for oneself.Insomnia symptoms were measured using the Polish version of the Athenian Insomnia Scale.Results: The correlation coefficients indicated positive relationships between the fear of COVID-19 infection and insomnia and cyberchondria, while age correlated negatively with cyberchondria.The hierarchical multivariate linear regression analysis revealed that COVID-19-related fear was the best predictor of cyberchondria.Insomnia and age were also cyberchondria predictors, but to a lesser extent.The mediation analysis revealed a significant indirect relationship between COVID-19-related fear and cyberchondria through insomnia symptoms.Conclusions: We observed that COVID-19-related fear and, to a lesser extent, age and insomnia were cyberchondria predictors.We also found both direct and indirect relationships between COVID-19-related fear and cyberchondria through insomnia.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".