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Record W4310866789 · doi:10.3389/feduc.2022.1008461

Rethinking conceptions of body image in group fitness education, culture, and contexts: Recommendations for perspective transformation and innovations in instructional methods

2022· article· en· W4310866789 on OpenAlexaff
Emily Dobrich

Bibliographic record

VenueFrontiers in Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)PerceptionPsychologyContext (archaeology)Action (physics)Social psychologyClass (philosophy)Physical fitnessApplied psychologyPedagogyComputer scienceMedicinePhysical therapyArtificial intelligence

Abstract

fetched live from OpenAlex

This article examines the situation of group fitness instructors with particular attention to the implications of the influence of fitness culture on body-related norms which shape instructors’ self-perceived conceptions of body image. Of particular interest is the consideration of how self-perception influences an instructor’s performance, and their ability to educate and motivate their class participants. Evidence will show that the most popular ways that body image is incorporated into and represented within the group fitness setting are limiting and misguided, and there are better methods for instruction that fitness professionals and the industry can follow. Recommendations for practice and suggestions for interventions to encourage adequate body satisfaction in the group fitness instructor’s context will be provided for both individual and collective levels of action. This will include what instructors can do at an individual level to improve their self-perceptions and professional practice and support themselves and their peers; what can be done in gyms and fitness facilities to improve community support for instructors; and what can be done at the fitness industry level to encourage a cultural shift in body-related norms and expectations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.489
Teacher spread0.436 · 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 designTheoretical or conceptual
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

Citations2
Published2022
Admission routes1
Has abstractyes

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