The effects of excessive and compulsive online searching of COVID-19 information (“cyberchondria”) on general and COVID-19-specific anxiety and fear in romantic couples during lockdown
Bibliographic record
Abstract
Introduction Cyberchondria involves excessive and uncontrollable online searching of information about a perceived illness. This behavior can cause or maintain distress. Objectives Little is known about cyberchondria during the COVID-19 pandemic or how cyberchondria in one individual may cause distress in their significant other if they are self-isolating together; our study sought to fill these gaps. Methods We conducted a Qualtrics Panel survey with 760 cohabitating Canadian couples; in June 2020, participants retrospectively reported on their cyberchondria behavior, general anxiety, and COVID-19 fears during the month of April 2020, while adhering to stay-at-home advisories. Two separate actor-partner interdependence models (APIMs) used cyberchondria excessiveness and compulsion to predict generalized anxiety and COVID-19 danger/contamination fears in the actor and partner. Results Both cyberchondria excessiveness and compulsion were associated with higher general anxiety and higher COVID-19 danger/contamination fears in the individual (actor effects). Partner cyberchondria compulsion was associated with higher general anxiety in the individual whereas partner cyberchondria excessiveness was associated with higher COVID-19 danger/contamination fears in the individual (partner effects). Conclusions Findings suggest that excessive and uncontrollable searching of information about COVID-19 on the internet during lockdown may contribute to distress in both the individual engaging in the cyberchondria behavior, and in their romantic partner. Moreover, different aspects of cyberchondria in the partner (compulsion vs. excessiveness) appears to contribute to general vs. COVID-19-specific anxiety/fears in the partner, respectively. Future research should examine mechanisms underlying the observed partner effects (e.g., co-rumination, social contagion) and reasons for the differential partner effects of cyberchondria components.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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, 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".