Separated cycling routes on cyclist safety: Reply to: Bicycling injury hospitalisation rates in Canadian jurisdictions: analyses examining associations with helmet legislation and mode share
Bibliographic record
Abstract
Globally, there is a strong movement to encourage active lifestyles in our communities. Clinicians, the public health community and policy makers around the globe are encouraging communities to engage in active lifestyles. Dr.Vivek Murthy, the US Surgeon General, is convinced of the benefits of active lifestyles in our communities and he encourages communities to embrace healthy behaviors (1). Furthermore, the Surgeon General aims to increase active lifestyles across the nation by calling for access to safe and convenient places to engage in these activities (1). Dr. Murthy rightly pointed out that it is not simply enough to encourage active lifestyles on its own without thinking about the importance of the built environment (1). A recent Canadian study by Teschke and her colleagues (2015) rightly recommend that the policy makers need to focus on bike infrastructure to promote safer cycling in our communities (2). We also believe that the efforts to improve cyclist safety cannot succeed without making substantial improvements to the current built environment. Despite substantial evidence base documenting the efficacy of separated cycling routes on cyclist safety (3) there is a lack of funding or investment to implement this evidence based cyclist injury prevention strategy. Research also shows that safer cycling infrastructures increase cycling rates significantly (4). References: (1) Step It Up! The Surgeon General's Call to Action to Promote Walking and Walkable Communities. Available online at: http://www.surgeongeneral.gov/library/calls/walking-and-walkable- communities/ (2) Teschke K, Koehoorn M, Shen H, Dennis J. Bicycling injury hospitalisation rates in Canadian jurisdictions: analyses examining associations with helmet legislation and mode share.BMJ Open. 2015; 2;5(11):e008052. doi: 10.1136/bmjopen-2015-008052. (3) Reynolds CC, Harris MA, Teschke K, Cripton PA, Winters M. Environ Health. 2009 ;21;8:47. (4) Winters M, Brauer M, Setton EM, Teschke K. Built environment influences on healthy transportation choices: bicycling versus driving. J Urban Health. 2010;87(6):969-93. Language: en
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".