Exploring coaches understanding of body image and weight inclusivity in youth sport: Implications for sport coach development and training
Bibliographic record
Abstract
Coaches are tasked with creating sport environments that facilitate the positive development of athletes. However, coaches are also described as portraying negative or maladaptive attitudes about athletes’ body shapes, weights, and performances. These attitudes lead to appearance-focused sport environments that discriminate against people in heavier bodies and contribute to poor sport experiences and dropout. A body image training program would benefit coaches, however, how coaches conceptualize body image and weight inclusivity, as well as their previous learning attempts on the topics, are not known. The current study sought to (1) explore how sport coaches conceptualize body image and weight inclusivity, (2) identify knowledge generation and learning on these topics, and (3) describe strategies to facilitate coach development on these topics. Six (50% women) coaches from across Canada were recruited to participate in 90-minute discussions. Data were collected and analyzed using a constructivist paradigm and thematic analyses. Coaches demonstrated limited understanding of weight inclusivity yet discussed some knowledge of body image. Despite the known relationship between body image and sport, most coaches could not recall any explicit attempts to create more weight and body-inclusive and positive body image sport environments. Furthermore, no training programs on these topics have been undertaken but coaches stressed a need for a brief, cost effective program to balance messages about the importance and value of weight inclusivity and body image in youth sport. Coaches also suggested practical strategies and guidelines for addressing these foundational topics.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".