Prevalence and sport‐related predictors of disturbed eating attitudes and behaviors: Moderating effects of sex and age
Bibliographic record
Abstract
Very few studies examined the prevalence and sport-related predictors of disturbed eating attitudes and behaviors (DEABs) among adolescents involved in sport practice, and their results are mixed and inconclusive. These inconsistencies are most likely due to their methodological heterogeneity and to the fact that none of these studies took into consideration the potentially relevant characteristics of the sport practice context. This study attempts to answer this limitation among French adolescents not involved or involved in various sports contexts defined based on their organization, leanness-centration, and competitive level. Participants were 335 adolescents involved in sport practice, and 435 adolescents not involved in any form of regular sport practice. The DEABs were measured using the Eating Attitudes Test-26. Global results do not showed any significant association between the status of the participants and DEAB. However, these results drastically changed when we considered the potential moderating role of sex and age on these relations. Indeed, sports involvement in general, and involvement in leanness and competitive sports were found to exert sex- and age-differentiated effects on the risks of presenting clinically significant levels of DEAB. This study suggests the importance of monitoring, preventive, and early intervention mechanisms within the context of practice, particularly for adolescent girls.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".