A systematic review of body image dissatisfaction in young \nathletes and non-athletes, and an empirical study of the \nlink between disgust and body image in an analogue \nsample
Bibliographic record
Abstract
Body image dissatisfaction (BID) commonly develops in adolescence. Young athletes may \nbe at a greater risk of developing BID than their non-athlete peers as a result of the unique \npressures of sporting contexts. This paper presents a systematic review of the recent literature \ncomparing athlete and non-athlete children and adolescents on a measure of BID, to explore \nthe current status of BID in young male and female athletes. Literature searches were \nconducted across five electronic databases. Eleven studies were narratively synthesised, and \nquality assessed using the Newcastle-Ottawa Quality Assessment Scale (NOQA). Most \nstudies were assessed to be of ‘fair’ or ‘poor’ quality, with two studies deemed to be of \n‘good’ methodological quality. Results indicated that engaging in structured and competitive \nsports may provide some buffer from BID, and that females experience greater BID than their \nmale peers regardless of athletic status. However, no clear pattern relating to the impact of \nage, sport type or competition on this buffer could be established. Further robust research \nwithin this field is warranted. Implications of the findings for BID in children and adolescents \nare discussed, along with future directions for research
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".