Redefining Body-Self Relationships Through Outdoor Physical Activity: Experiences of Women Navigating Illness, Injury, and Disability
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".