Way2Go! Social marketing for girls' active transportation to school
Bibliographic record
Abstract
Active transportation to school (ATS) is a recognized way to increase physical activity (PA). However, girls and young women do not regularly use ATS despite the many documented physical, mental, and community health benefits. Social Marketing (SM) may provide a framework for understanding girls' perspectives of and experience with ATS and inform messages for use in a public health marketing campaign. Focus groups with 79 girls between the ages of 7 and 15 were conducted in Spring 2017 in Victoria, Canada. Transcripts and poster data were initially categorized using the '4Ps' from social marking (Product, Price, Place and Promotion). Participant groups were segmented into three age categories for designing tailored messaging. Thematic analysis revealed elementary school aged participants identified health and fun while middle school participants valued socializing and helping the environment as reasons for engaging in ATS. For secondary school students, ATS was seen as a way to become more independent. All three highlighted fun and enjoyment as important benefits of ATS, and suggested positive and lighthearted messaging. Segmenting into different audiences highlighted how campaign segmentation would resonate with different audiences based on core values and beliefs. Further segmentation of the audience could result in different core values and beliefs held by diverse groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.136 | 0.015 |
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".