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Record W3131011049 · doi:10.1177/1356336x21991181

Using discussion to inform action: Formative research on nature-based physical activity as a means of fostering relatedness for girls in physical and health education

2021· article· en· W3131011049 on OpenAlexaff
Jennifer Gruno, Sandra Gibbons

Bibliographic record

VenueEuropean Physical Education Review · 2021
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDisengagement theoryFormative assessmentPsychologyPhysical educationPsychological interventionAction researchIncentivePedagogyMedical educationFocus groupIntervention (counseling)Health promotionMedicineSociologyGerontologyPublic healthNursing

Abstract

fetched live from OpenAlex

The long-standing challenges and issues associated with girls’ disengagement from secondary school physical and health education (PHE) are serious and well documented. This disengagement has provided the incentive for the examination of alternative strategies to facilitate girls’ engagement in PHE. This paper discusses the first phase in a formative research process designed to develop a resource manual to help teachers utilize nature-based physical activity (NBPA) as a means of fostering relatedness for girls in PHE. Participating teachers collaborated and generated specific NBPA ideas and pedagogical strategies during an all-day planning session. Four focus groups with the teachers ( N = 20) were used to identify ways to develop NBPA interventions. Five broad topics are reported: (a) defining NBPAs, (b) specific NBPAs to use in PHE, (b) how NBPA can foster relatedness, (d) how NBPA in PHE differs from outdoor education, and (e) barriers to implementing NBPA in PHE. This paper emphasizes the valuable contribution of formative research to the integrity and fidelity of an intervention as well as to quality practice in the implementation of theory-based PHE initiatives.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.245
GPT teacher head0.577
Teacher spread0.332 · 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 designOther design
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

Citations3
Published2021
Admission routes1
Has abstractyes

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