Pandemic-related anxiety and screen time: A mediation analysis
Bibliographic record
Abstract
ABSTRACT Background The COVID-19 pandemic brought worldwide lockdowns and social distancing, causing feelings of pandemic-related anxiety and consequentially poorer mental health and loneliness. While social isolation and poor mental health have both been previously linked to increased screen time, it is unclear if they can explain the increased screen time during the pandemic. Objective This study investigated whether pandemic-related anxiety is associated with increased screen time, and whether this relationship is mediated by an increase in internalizing and externalizing symptoms, as well as loneliness. Methods 572 Canadian participants (average age 27.60) completed an N survey between June 2020 to November 2021. The survey measured pandemic-related anxiety, emotional and behavioral symptoms, and loneliness. Participants also used a mobile sensing app over two weeks to record their daily objective screen time. A structural equation model assessed the relationship of pandemic-related anxiety with general mental health and loneliness, as well as the relationship between these psychological constructs and objective daily screen time. Results Pandemic-related anxiety was associated with greater screen time. Externalizing symptoms and loneliness mediated the association of screen time with worries about the consequences of the pandemic, but not with worries about contracting the disease. Conclusions Worrying about contracting the disease is an independent risk factor in developing more concerning patterns of screen use. Additionally, worrying about the consequences of the pandemic is not an independent factor but rather is mediated by externalizing symptoms and loneliness. This has implications for conceptualizing problematic screen use and the development of intervention and prevention efforts.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".