The impact of the intensity of media use on potential tourists’ risk perception and travel protective behavioral intentions in COVID-19
Bibliographic record
Abstract
Introduction: In light of the COVID-19 pandemic, there is an increased need for potential travelers to gather information about their trips to mitigate perceived risks. This study aims to understand the relationship between the intensity of media use (both new and traditional), epidemic risk perception, and tourism protection behavior intention among potential tourists. Methods: A total of 491 valid questionnaires were collected in Shanghai, China. Factor analysis, path analysis, and effect analysis were conducted using SPSS and AMOS to examine the impact of different media types on epidemic risk perception and tourism protection behavior. Results: The findings indicate a positive association between new media use intensity and epidemic risk perception, as well as an intention to adopt safety-conscious tourism behaviors. In contrast, traditional media usage is inversely associated with risk perception but has no significant influence on protective behavior. The results also highlight the role of demographic factors, such as age, education level, occupation, and income, in modulating the relationship between media usage and risk perception. Discussion: The contrasting effects of new and traditional media suggest the need for a tailored approach in epidemic communication strategies. Public health officials should leverage new media to enhance risk perception and safety-oriented behaviors, while recognizing the role of traditional media in managing lower risk perceptions and assuaging panic. The study emphasizes the importance of personalized messaging based on demographic disparities in media usage and perception. The mediating role of risk perception in shaping protective behaviors offers insights for promoting adherence to safety protocols. Conclusion: This study contributes to a comprehensive understanding of media influences during health crises, emphasizing the responsibility of media platforms in transmitting accurate information. The findings call for a nuanced approach to epidemic communication, considering the strengths and weaknesses of different media types. Segmented and personalized messaging strategies can cater to demographic variations in media usage and perception. Enhancing risk perception through tailored messaging can promote protective behaviors and effectively manage public sentiment during health crises.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".