Evaluating a bicycle education program for children: Findings from Montreal, Canada.
Bibliographic record
Abstract
Promoting bicycle usage amongst school-aged children is one way to encourage more active lifestyles. The purpose of this paper is to evaluate an on- and off-bicycle education program for school-aged children in Montreal, Canada with the goal of understanding how education influences children’s and parents’ cycling behavior and attitudes. Using qualitative measures and descriptive statistics this paper analyzes pre- and post-program survey results from children who participated in the program and their parents. Results show that children’s knowledge of bicycle safety increased and that participants made significant improvements in knowing bicycle-specific street signs (before: 83%, after: 92%) and hand signals (before: 68%, after: 96%). Children also became more confident: before the program 75% of children stated that riding a bicycle was not difficult for them and after, this increased to 92%. Students’ parents also reported improvements in their children’s cycling abilities, and 55% stated that they would allow their children to participate in an organized “cycle-to-school” program. In addition, half of the parents included in the post-program survey stated that their behaviors and/or attitudes towards cycling had positively changed as a result of their child’s involvement in the bicycle education program. To encourage cycling in any region, bicycle educators and advocacy groups need to develop school-based bicycle education programs as well as “cycle-to-school” programs. City planners should consider implementing policies that encourage the development of bicycle infrastructure and traffic calming measures, especially near schools in order to encourage parents to allow their children to bicycle to school.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".