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Record W4412640066 · doi:10.3390/bs15081006

Redefining Body-Self Relationships Through Outdoor Physical Activity: Experiences of Women Navigating Illness, Injury, and Disability

2025· article· en· W4412640066 on OpenAlexaff
Joelle Breault-Hood, Tonia Gray, Jacqueline Ullman, Son Truong

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPhysical activityMedicinePsychologyPhysical therapyGerontology

Abstract

fetched live from OpenAlex

Physical challenges such as illness, injury, and disability significantly alter women’s relationships with their bodies, disrupting established notions of functionality and self-worth. This study re-examines the Holistic Model of Positive Body Image and Outdoor Physical Activity through secondary analysis focusing on women with illness, injury, and disability. From the original sample of N = 553 female participants, open-ended survey responses were identified from n = 84 participants (15.2%) who self-disclosed as having illness, injury, or disability to examine how outdoor settings facilitate positive body image. Through reflexive thematic analysis, the study revealed three key mechanisms: (1) personalized redefinition of functionality transcending standardized metrics, (2) therapeutic engagement with natural environments fostering embodied acceptance, and (3) cyclical reinforcement between physical capability and psychological wellbeing. The findings confirm the model’s utility while indicating necessary adaptations to address the fluctuating nature of body functionality. The adapted model emphasizes how outdoor recreational activities create contexts for reimagining body-self relationships across the spectrum of physical experiences—from temporary recovery to ongoing adaptation of persistent conditions—with implications for rehabilitation professionals, outdoor educators, and healthcare providers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.070
GPT teacher head0.413
Teacher spread0.343 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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