Engaging With the Community to Promote Physical Activity in Urban Neighborhoods
Bibliographic record
Abstract
PURPOSE: To describe perceptions of physical activity, opinions, on intergenerational approaches to physical activity and a vision for increasing physical activity in an underresourced urban community. APPROACH: Focus groups embedded in a large Community-Based Participatory Research Project. SETTING: West and Southwest Philadelphia. PARTICIPANTS: 15 parents, 16 youth, and 14 athletic coaches; youth were 13 to 18 years old and attended West Philadelphia schools; parents' children attended West Philadelphia schools; and coaches worked in West Philadelphia schools. METHODS: Six focus groups (2 youth, 2 parent, and 2 coach) were conducted guided by the Socio-Ecological Model; transcriptions were analyzed using a rigorous process of directed content analysis. RESULTS: Factors on all levels of the Socio-Ecological Model influence the perception of and engagement in physical activity for youth and their families. Future strategies to increase engagement in physical activity need to be collaborative and multifaceted. CONCLUSION: When physical activity is reframed as a broad goal that is normative and gender-neutral, a potential exists to engage youth and their families over their lifetimes; with attention to cross-sector collaboration and resource sharing, engaging and sustainable intergenerational physical activity interventions can be developed to promote health in underresourced urban communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".