Understanding the Post‐Pandemic Travel Intentions Among Chinese Residents: Impact of Sociodemographic Factors, <scp>COVID</scp> Experiences, Travel Planned Behaviours, Health Beliefs, and Resilience
Bibliographic record
Abstract
ABSTRACT We investigate the shift of travel intentions among Chinese residents following the end of China's Zero‐COVID policy in December 2022. Focusing on the 2023 Spring Festival, the first major holiday after the pandemic, we examine the factors influencing travel intentions, including travel experiences during COVID‐19, sociodemographics, infection and vaccination experiences, travel planned behaviours, health beliefs, and resilience. Using a cohort study approach, we conducted online surveys in two phases. Initial findings from 1, 263 respondents pre‐holiday indicated a moderate intention to travel (average 3.3 out of 5). The results reveal diminishing effects of COVID‐19 vaccination, infection experiences and health beliefs (perceived susceptibility, severity and benefits) over time. Past travel experiences, planned behaviours (attitudes, subjective norms and perceived behavioural control), perceived barriers and resilience significantly elevate travel intentions in the post‐COVID period. Additionally, a post‐holiday survey found that 44.3% of 79 participants had travelled, providing insight into the evolving travel tendencies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".