Applying the multiphase optimization strategy to evaluate the feasibility and effectiveness of an online road safety education intervention for children: A pilot study
Bibliographic record
Abstract
Abstract Background: Reports of children’s engagement in active transportation (e.g., cycling, walking, wheeling) outline low participation rates in many countries despite many associated mental, physical, and social health benefits. One of the main contributors to this phenomenon is a cited lack of education and knowledge among children regarding active travel (AT) and its specific related modalities. Moreover, reviewed AT interventions have been critiqued for lacking comprehensiveness in their designs, especially as it relates to various education strategies. To address these issues, the aim of this study was to evaluate the feasibility and effectiveness of an online road safety education intervention to promote AT among children (ages 9-13). Methods: Applying the Multiphase Optimization Strategy (MOST) for intervention development, implementation, and evaluation, we designed and assessed a four-module online road safety education intervention with a sample of 56 children using a 23 factorial design featuring both qualitative and quantitative analyses. Results: Main intervention feasibility findings include positive and critical feedback on the program’s content and design, and moderate participant engagement as reflected by program retention and completion rates. With respect to intervention effectiveness, a significant improvement in road safety knowledge scores was observed for groups that feature the “wheeling safety and skills” module (p<0.05). Although there was a slight improvement in AT knowledge scores across all the intervention groups, differences in scores were not of significance (p>0.05). Conclusion: The MOST framework allowed us to design and evaluate the feasibility and effectiveness of an efficient multicomponent online road safety education intervention. As a result, the developed intervention has demonstrated that it has the potential to improve children’s road safety knowledge, to which improvements may be attributed to the inclusion of the “wheeling safety and skills” module, further suggesting that the targeted focus on cycling skills is a prioritized area amongst children. Implications for AT program developers and evaluators are discussed. Future research is encouraged to develop strategies that target AT knowledge and awareness topics (e.g., recognition of associated benefits).
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".