Personal risk factors and types of sport associated with drive for thinness and drive for muscularity in NextGen athletes
Bibliographic record
Abstract
Introduction: ) who compete at the provincial, national, and international levels but are not yet part of national teams. As such, they have access to fewer financial resources and support from their sport federations, which can create additional stress for these athletes. The present study aimed to identify personal risk factors and types of sports associated with (a) drive for thinness and (b) drive for muscularity in NextGen athletes. Methods: = 254) aged between 14 and 25 years old completed an online questionnaire. Perfectionism, self-esteem in sport, drive for thinness, and drive for muscularity were, respectively, assessed by the Multidimensional Inventory of Perfectionism in Sport, the Sport State Self-Esteem Scale, the Eating Attitude Test-26, and the Drive for Muscularity Scale. Other personal risk factor (e.g., athletic status) were measured using in-house questions. Each personal risk factor was included in two multiple linear regressions, respectively, to determine which factors were most associated with drive for thinness and drive for muscularity. Results: Analyses revealed that perfectionist aspirations during training were linked to a stronger desire for thinness. In addition, not being in school or not having a job were also linked to a stronger desire for thinness. Several variables were found to be associated with a greater desire for muscularity: being a male athlete, playing team sport, weight category sport or endurance sport (as opposed to esthetic sport), having elite status, being less satisfied with one's current sporting performance and having higher perfectionist aspirations during training. Discussion: This study offers initial insights into the factors influencing EDs among NextGen athletes, which provides a foundation for deeper exploration and the creation or modification of targeted interventions. These findings can guide sport organizations in creating guidelines and programs to better support the next generation of national athletes (e.g., create programs to help athletes maintain investments outside).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".