Creating Healthy Schools and Student Engagement: The Got Health? Initiative
Bibliographic record
Abstract
The purpose of this paper is to discuss the findings from a study entitled Got Health? and its initiative to discover how student-led health inquiry projects lead to healthy school environments, student engagement and connectedness. The intention was to empower students to create healthy change in their school settings by providing them with training, teacher guidance and opportunities to be change agents. Ten schools participated in the study. Each school identified a teacher champion and a team of students to actively address and promote health issues through student led inquiry projects. Semi-structured focus groups were used to collect data and a framework analysis approach was used for analysis. Results revealed that most participants gained a sense of connectedness to their school and peers, improved their health awareness and facilitated student engagement. With adult-led support, schools should consider utilizing student-led initiatives to assist in health-related activities.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".