Media Use During the COVID-19 Pandemic: Cross-sectional Study
Bibliographic record
Abstract
Background Throughout the pandemic, the general population was encouraged to use media to be kept informed about sanitary measures while staying connected with others to obtain social support. However, due to mixed findings in the literature, it is not clear whether media use in such a context would be pathogenic or salutogenic. Objective Therefore, the associations between COVID-19–related stressors and frequency of media use for information-seeking on trauma- and stressor-related (TSR) symptoms were examined while also investigating how social media use for support-seeking and peritraumatic distress interact with those variables. Methods A path model was tested in a sample of 5913 adults who completed an online survey. Results The number of COVID-19–related stressors (β=.25; P<.001) and extent of information-seeking through media (β=.24; P=.006) were significantly associated with the severity of TSR symptoms in bivariate comparisons. Associations between levels of peritraumatic distress and both COVID-19–related stressors and information-seeking through media, and social media use for support- and information-seeking through media were found (βCOVID-19 stressors: Peritraumatic Distress Inventory=.49, P<.001; βseeking information: Peritraumatic Distress Inventory=.70, P<.001; βseeking information–seeking support=.04, P<.001). Conclusions Results suggest that exposure to COVID-19–related stressors and seeking COVID-19–related information through the media are associated with higher levels of peritraumatic distress that, in turn, lead to higher levels of TSR symptoms. Although exposure to the stress of the COVID-19 pandemic may be unavoidable, the frequency of COVID-19–related information consumption through various media should be approached with caution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".