Parkour as Health Promotion in Schools: A Qualitative Study on Health Identity
Bibliographic record
Abstract
In the present paper, we highlight the potential role of parkour in school-based health promotion. In a school setting,it is often difficult to promote health and healthy behaviour in ways that make sense and appeal to pupils. Researchsuggests that initiatives incorporating a focus on identity and on presenting health in new and different ways aremore likely to succeed in generating engagement, participation and involvement and thereby to affect learningoutcomes and behaviour change. In the present paper, we explore and discuss parkour as just such a new anddifferent approach. We do this using an empirically and theoretically tested concept of health identity as our maintheoretical and analytical component. We present our findings in three main themes: 1) Changed self-images provideopportunities for social inclusion, 2) New observations of others force pupils to reconsider roles and hierarchies and3) Togetherness and non-competitiveness generate a sense of belonging. The paper provides teachers and schoolhealth practitioners with important knowledge about why they may wish to incorporate parkour into school healthpromotion and equally important knowledge about how a focus on health identity is essential if they are to ensureconditions that facilitate significant health-promoting effects for all pupils – not just for those who are already healthy.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".