Lockdowns and cycling injuries: temporal analysis of rates in Quebec during the first year of the pandemic
Bibliographic record
Abstract
INTRODUCTION: Cycling increased in popularity during the COVID-19 pandemic, but the impact on cycling injuries is not known. We examined the effect of lockdowns on cycling injury hospitalizations. METHODS: We identified hospitalizations for cycling injuries in Quebec, Canada, between April 2006 and March 2021. We used rate ratios (RR) and 95% confidence intervals (CI) to compare hospitalization rates by type of cycling injury and anatomical site during two waves of the pandemic. We performed interrupted time series regression to assess the effect of lockdowns on monthly cycling injury hospitalization rates, according to age, sex and other characteristics. RESULTS: There were 2020 hospitalizations for cycling injuries between March 2020 and March 2021, including 617 during the first lockdown and 67 during the second lockdown. Compared with the period before the pandemic, risk of cycling-related injuries during the first lockdown increased the most for fractures (RR = 1.44; 95% CI: 1.26- 1.64) and head and neck injuries (RR = 1.59; 95% CI: 1.19-2.12). Cycling injury hospitalization rates increased significantly among adults, adolescents and individuals from socioeconomically advantaged neighbourhoods or those with low concentrations of racialized people every month of the first lockdown. The second lockdown was not associated with cycling injuries. CONCLUSION: The first lockdown triggered a sharp increase in cycling injury hospitalizations, especially among adults, adolescents and individuals from socioeconomically advantaged and less racialized neighbourhoods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".