Understanding COVID-19 pandemic-related shifts in active commute patterns: Insights from employees of a Canadian university
Bibliographic record
Abstract
It is well established that the COVID-19 pandemic profoundly disrupted the commute patterns of people worldwide. Yet, little is known about how commuting by active transportation (AT) has shifted since COVID-19 restrictions lifted. This quasi-experimental study aimed to: 1) compare AT mode share pre-versus post-COVID-19 pandemic; and 2) identify key post-pandemic correlates of transport mode. A survey of employees from a Canadian university was conducted annually from 2013 to 2017 and in 2022. There were 644 valid participants who completed at least one of the surveys pre-COVID-19 pandemic and in 2022. Participants were categorized as using AT, passive transportation (PT), or mixed transportation (MT) as their primary transport mode between home and workplace. The mode share of each transport type pre-versus post-COVID-19 was analyzed. Additionally, shifts in individual-level transportation modes and related sociodemographic correlates were evaluated. Compared to pre-COVID-19 pandemic, AT decreased from 27.0% to 23.4%; however, PT increased from 52.0% to 60.7% in the post-pandemic era. Household income, age, and sex/gender were key correlates of transport mode shifting. Only the lowest income group showed an increase in AT (18.8%–20.3%); all other groups showed no change or a decrease, with the greatest decline observed in those earning $90,000 to $119,999 (20.5%–13.5%). AT use decreased in all age groups under 50 years with the clearest change in the 20- to 29-year-old age range (24.3%–8.1%). Females/women used AT at half the rates of males/men, both pre- and post-COVID-19. Queen's university's employees demonstrated changes in transport mode use due to the COVID-19 pandemic. The decline in AT coupled with an increase in PT shown in this sample emphasizes the importance of organizational- and/or municipal-level interventions to encourage transportation modes that are both sustainable and health-promoting in the post-pandemic era. • Active transportation (AT) decreased and passive transportation (PT) increased post-COVID-19 pandemic. • Younger age associated with lowest AT post-COVID-19. • Lower income and younger age associated with higher mode shifting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".