Policy influencer support for active transportation policy in two Canadian provinces: Implications for advocacy
Bibliographic record
Abstract
Active travel or transportation (AT) is recognized for its growing potential to improve the health and wellbeing of Canadians. AT policy helps to promote active commuting by reducing barriers, such as through the development of infrastructure and reducing traffic. As policy influencers are important gatekeepers who can promote or impede policy, this research explores policy influencer support for AT policy in two Canadian provinces. The Chronic Disease Prevention Survey has examined policy influencer and general public support for healthy public policy options since 2009. The 2019 iteration surveyed policy influencers in Alberta (n = 248) and Manitoba (n = 115) working in government and non-government organizations. These policy influencers were asked to indicate their level of support for eight AT policy options and demographic information. Descriptive statistics were used to analyze support for AT policy options and demographics. Different levels of support between policy influencers by province and group were determined by a two-sided Pearson's chi-squared test. Respondents indicated high levels of support across all policy influencer groups. Four policy options demonstrated significantly lower support among Alberta respondents compared to Manitoba. Respondents in government and non-government groups reported similar levels of support for all but one policy option (Ensure municipalities establish minimum standards for health promoting environments that developers need to address; 78.3% vs. 91.2% respectively). Overall levels of support for active school transportation were high across respondent groups (average 96.0%). By understanding the current levels of support, advocates can appropriately garner support and tailor advocacy plans. Key takeaways: (1) school-based policies could be a quick win for AT advocates and (2) work is needed to build support for more intrusive but impactful AT policy options. Overall, the high levels of support by policy influencer respondents across most AT policy options indicate a potential policy window for advocates.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".