COVID-19: Are you satisfied with traveling during the pandemic?
Bibliographic record
Abstract
The outbreak of COVID-19 and preventive measures to limit the spread of the virus has significantly impacted our daily activities. This study aims to investigate the effect of daily activity engagement including travel activity and sociodemographic characteristics on travel satisfaction during COVID-19. This study develops a latent segmentation-based ordered logit (LSOL) model using data from the 2020 COVID-19 Survey for Assessing Travel Impact (COST), for the Kelowna region of British Columbia, Canada. The LSOL model accommodates the ordinal nature of the satisfaction level and captures heterogeneity by allocating individuals into discrete latent segments. The model results suggest that the two-segment LSOL model fits the data best. Segment one is more likely to be younger and older high-income workers; whereas, segment two includes middle-aged lower-income, unemployed individuals. The model results suggest that daily activity engagement and sociodemographic attributes significantly affect travel satisfaction. For example, participation in travel for routine shopping, recreational activity, and household errands has a positive effect on travel satisfaction. The use of transportation modes like bike/walk depicted a higher probability to yield travel satisfaction. The model confirms the existence of significant heterogeneity. For instance, travel for work showed a negative relationship in segment one; whereas, a positive relationship is found in segment two. Access to higher household vehicle yield lower satisfaction in segment one; in contrast, a positive relationship is found in segment two. The findings of this study provide important insights towards maintaining the health and well-being of the population during this and any future pandemic crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".