Moderating Effect of eHealth Literacy on the Associations of Coronaphobia With Loneliness, Irritability, Depression, and Stigma in Chinese Young Adults: Bayesian Structural Equation Model Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led to an increase in known risk factors for mental health problems. Although medical information available through the internet and smartphones has greatly expanded, people's ability to seek, eschew, and use reliable web-based medical information and services to promote their mental health remains unknown. OBJECTIVE: This study aims to explore the associations between coronaphobia and 4 frequently reported mental health problems, loneliness, irritability, depression, and stigma, during the COVID-19 pandemic and to assess the moderating effects of eHealth literacy (eHL) on the adjustment of these relationships in Chinese young adults. METHODS: The data used in this study were collected from a web-based survey of the general Chinese population, aged between 18 and 30 years, conducted in China between December 2022 and January 2023. A nonprobability snowball sampling method was used for data collection. A Bayesian structural equation model (BSEM) using parameter expansion was used to estimate the moderating effect of eHL on the relationship between coronaphobia and psychological problems. The posterior mean and 95% highest density intervals (HDIs) were estimated. RESULTS: A total of 4119 participants completed the questionnaire and provided valid responses. Among them, 64.4% (n=2653) were female and 58.7% (n=2417) were rural residents. All measures showed statistically significant but minor-to-moderate associations (correlation coefficients ranged from -0.04 to 0.65). Significant heterogeneity was observed between rural and urban residents at the eHL level, and coronaphobia was observed. The BSEM results demonstrated that eHL was a significant moderator in reducing the negative effects of coronaphobia on loneliness (posterior mean -0.0016, 95% HDI -0.0022 to -0.0011), depression (posterior mean -0.006, 95% HDI -0.0079 to -0.004), stigma (posterior mean -0.0052, 95% HDI -0.0068 to -0.0036), and irritability (posterior mean -0.0037, 95% HDI -0.0052 to -0.0022). The moderating effects of eHL varied across the rural and urban subsamples. CONCLUSIONS: Using BSEM, this study demonstrated that improving eHL can significantly mitigate the negative effects of coronaphobia on 4 COVID-19-related mental health problems in Chinese young adults. Future eHL initiatives should target rural communities to ensure equal access to information and resources that can help protect their mental health during the pandemic.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".