Factors Impacting Chinese Older Adults’ Intention to Prevent COVID-19 in the Post–COVID-19 Pandemic Era: Survey Study
Bibliographic record
Abstract
BACKGROUND: Understanding the factors influencing individuals' health decisions is a dynamic research question. Particularly, after China announced the deregulation of the COVID-19 epidemic, health risks escalated rapidly. The convergence of "no longer controlled" viruses and the infodemic has created a distinctive social period during which multiple factors may have influenced people's decision-making. Among these factors, the precautionary intentions of older individuals, as a susceptible health group, deserve special attention. OBJECTIVE: This study aims to examine the intention of older adults to engage in preventive behaviors and the influencing factors, including social, media, and individual factors, within the context of the postepidemic era. Drawing upon the structural influence model of communication, this study tests the potential mediating roles of 3 different types of media exposure between cognitive and structural social capital and protective behavior intention, as well as the moderating role of negative emotions between social capital and media exposure. METHODS: In this study, a web survey was used to collect self-reported quantitative data on social capital, media exposure, negative emotions, and the intention to prevent COVID-19 among older adults aged ≥60 years (N=399) in China. RESULTS: The results indicate that cognitive social capital significantly influenced protective behavior intention (P<.001), with cell phone exposure playing an additional impactful role (P<.001). By contrast, newspaper and radio exposure and television exposure mediated the influence of structural social capital on protective behavior intention (P<.001). Furthermore, negative emotions played a moderating role in the relationship between cognitive social capital and cell phone exposure (P<.001). CONCLUSIONS: This study suggests that using tailored communication strategies across various media channels can effectively raise health awareness among older adults dealing with major pandemics in China, considering their diverse social capital characteristics and emotional states.
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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 | 0.002 |
| 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".