A Structured, Interactive Method for Youth Participation in a School District-University Partnership to Prevent Obesity
Bibliographic record
Abstract
BACKGROUND: The involvement of school-age children in participatory research is described in the context of a school district-university partnership to prevent obesity in children. The purpose of this study was to elicit, from children in kindergarten (K) through sixth grade, perceptions of foods and activities that would inform the design of developmentally appropriate interventions to prevent and reduce childhood obesity. METHODS: Children (N = 218) were selected through a random sample of K through sixth grade classrooms in 3 schools. They participated in structured, interactive, small group exercises focused on perceptions of foods (taste and healthy/unhealthy) and activities (fun and active/sedentary). High school students in the same school district were trained to facilitate the children's groups in collaboration with university faculty and students. RESULTS: Qualitative data analysis was used to discern patterns across grade levels. There were grade-level differences in perceptions of the taste and healthfulness of foods. Younger children (K-1) equated foods that tasted good with foods that were "good for you." Older children were more discriminating and gave reasons for their perceptions. For activities, fun was positively associated with the number of people involved and the amount of movement. There were fewer differences across grade levels in preferences for types of sedentary activities, compared with sports and other activities that "make you move." CONCLUSIONS: The findings have implications for developmentally appropriate health promotion interventions to prevent obesity. These structured but highly interactive methods could be used by school personnel to assess the unique needs of a school population.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".