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Record W1607620176 · doi:10.5430/wje.v5n3p37

Parkour as Health Promotion in Schools: A Qualitative Study on Health Identity

2015· article· en· W1607620176 on OpenAlexvenueno aff
Dan Grabowski, Signe Dalsgaard Thomsen

Bibliographic record

VenueWorld Journal of Education · 2015
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsAppealHealth promotionIdentity (music)Promotion (chess)PsychologyInclusion (mineral)Focus groupQualitative researchHealth educationPublic relationsSocial psychologySociologyPublic healthMedicinePolitical scienceSocial scienceNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.523
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2015
Admission routes1
Has abstractyes

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