Promoting children’s health through community-led street interventions: analyzing sustained voluntarism in Canadian School Streets
Bibliographic record
Abstract
Abstract Background Active School Travel (AST) initiatives align with the Ottawa Charter for Health Promotion, which calls for ‘creating supportive environments’ and ‘strengthening community action.’ However, their reliance on volunteers poses sustainability challenges. The main objectives of this study were to document the motivations, satisfaction, and experiences of volunteers involved in sustaining two AST initiatives in Ontario for an entire school year. Methods Two volunteer-led School Street initiatives in Kingston, Ontario successfully operated during pick-up and drop-off times of each school day. The first initiative operated for the entire 2021-2022 school year, and the second operated for the entire 2022-2023 school year. These initiatives were the first of their kind in the province of Ontario, Canada. Volunteers from both sites (n = 56) participated in online surveys and their motivations, satisfaction, and experiences of their role were compared using the 2-sided Fisher’s Exact Test. Results Over 80% of volunteers were highly motivated to promote safety and over 70% of volunteers were highly motivated to disrupt the status quo of unsupportive, car-centric urban environments by reimagining how streets can be used. By taking collective action to re-shape the environment around these public schools to support healthy, active living, our findings reveal that over 90% of volunteers were highly satisfied. Of the volunteers, 87% felt they contributed to child safety and 85% felt they had developed stronger community connections. They appreciated the short (i.e., 40 minute) time commitment of each shift, weekly email communications by the community organization leading the initiative, and the volunteer schedule. They also appreciated the positive social interactions during volunteer shifts, which they felt outweighed the minimal resistance they experienced. Conclusions This research demonstrates the importance of logistical, motivational, and social factors in recruiting and retaining volunteers for community-led School Streets. Our findings support appealing to prospective volunteers’ influence in achieving School Street objectives (e.g., improved safety) in recruitment efforts, as well as highlighting School Streets’ innovative approach. Communicating with volunteers throughout School Street planning and implementation processes and limiting traffic in the closed street zone (i.e., by excluding the school staff parking lot and private driveways from the scope) are additional recommendations based on the findings of this study.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".