Reflections and projections: 25 considerations for body image research in sport and exercise psychology
Bibliographic record
Abstract
This paper presents 25 key considerations for advancing research and practice on body image within sport and exercise psychology. Drawing from foundational work and emerging trends, we identify five core areas for progress: conceptualization, measurement, theory, intervention, and policy. First, clarity of key concepts is emphasized, particularly in distinguishing body image from related constructs such as eating disorders, and in advancing our understanding of identity and intersectionality. Second, measurement considerations call for refining self-report tools, embracing diverse methodologies, and aligning study designs with cultural relevance and lived experience. Third, we trace the evolution of theoretical approaches that have been applied to understand the relationship between sport/exercise (dis)engagement and body image and introduce a novel dual-pathways framework to guide future research in sport and exercise contexts. Fourth, in terms of intervention, we highlight the need to develop and evaluate body image programs for athletes and sport leaders, promote movement for positive body image outcomes, and harness digital and media literacy strategies. Finally, we outline policy directions to foster positive body image environments and protect against harm in sport and exercise settings. This paper aims to serve as a roadmap for future work that is inclusive, innovative, and impactful. • Frameworks that account for social influences, dual pathways, and diverse populations are explored. • Conceptual and measurement distinctions within body image and sport and exercise are examined. • Theoretical integration of sociocultural, motivational, and positive psychology perspectives is discussed. • The need for multidimensional, inclusive, and intersectional approaches to body image programming is highlighted. • The role of collaboration among sport and exercise stakeholders in promoting positive body image is emphasized.
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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 | 0.112 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.039 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".