Levels and correlates of active transportation among Queen’s University employees pre- versus post-COVID-19 pandemic
Bibliographic record
Abstract
Introduction: Active transportation (AT) has dual benefits: population health benefits with increased physical activity and environmental sustainability with decreased carbon emissions. Despite the city’s low geographic density, residents of Kingston, Canada use AT for 9.5% of trips, compared to the country’s average of 6.9%. Additionally, employees of Queen’s University demonstrated even higher rates of active commuting before the COVID-19 pandemic. The objective of this study was to compare the levels and correlates of transportation use among Queen’s University employees pre- versus post-COVID-19 pandemic. Methods: Self-reported, repeated cross-sectional, and quasi-experimental data were used from the Surveys of Commute Patterns among Queen’s University Employees, conducted in 2013 to 2017 (n = 3874) and 2022 (n = 2430). Respondents’ primary mode of transportation between home and workplace was categorized as either AT, passive transportation (PT), or mixed transportation (MT) when different modes were combined. A descriptive analysis and chi-square test were performed to compare the levels of transportation use before and after the COVID-19 pandemic. A series of logistic regressions were conducted to identify the individual- and microsystem-level correlates of AT use before and after the COVID-19 pandemic. Results: AT use decreased after the COVID-19 pandemic, from 28.3% to 23.6%, mirroring the increased use of passive transport (52.1% to 55.4%). Within-subject comparisons showed that young and low-income respondents were the most likely to shift transport types from pre- to post-COVID-19 pandemic. At all time points, females/women were less likely to use AT than males/men. After the COVID-19 pandemic, income emerged as a key correlate of AT use. Conclusion: Significant world events like the COVID-19 pandemic appear to influence transportation choices and modes in unique ways among Queen’s employees. In general, AT was found to decline. Age and household income were important correlates for commute shifting since the COVID-19 pandemic. Sex/gender became a more important correlate in shaping transport mode post-COVID-19. To encourage shifting to more sustainable and health-enhancing transport modes among those with low and/or declining rates of AT, changes to supportive social environment and infrastructure must be considered in the Queen’s University community.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".