Effects of Social Media Use for Health Information on COVID-19–Related Risk Perceptions and Mental Health During Pregnancy: Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Social media has become an important source of health information during the COVID-19 pandemic. Very little is known about the potential mental impact of social media use on pregnant women. OBJECTIVE: This study aims to examine the association between using social media for health information and risk perception for COVID-19, worry due to COVID-19, and depression among pregnant women in China. METHODS: A total of 4580 pregnant women were recruited from various provinces of China. The participants completed a cross-sectional, web-based survey in March 2020. RESULTS: More than one-third (1794/4580, 39.2%) of the participants reported always using social media for obtaining health information. Results of structural equation modeling showed that the frequency of social media use for health information was positively associated with perceived susceptibility (β=.05; P<.001) and perceived severity (β=.12; P<.001) of COVID-19, which, in turn, were positively associated with worry due to COVID-19 (β=.19 and β=.72, respectively; P<.001). Perceived susceptibility (β=.09; P<.001), perceived severity (β=.08; P<.001), and worry due to COVID-19 (β=.15; P<.001) all had a positive association with depression. Bootstrapping analysis showed that the indirect effects of frequency of social media use for health information on both worry due to COVID-19 (β=.09, 95% CI 0.07-0.12) and depression (β=.05, 95% CI 0.02-0.07) were statistically significant. CONCLUSIONS: This study provides empirical evidence on how social media use for health information might have a negative impact on the mental health of pregnant women. Interventions are needed to equip this population with the skills to use social media properly and 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".