The Impact of Sports Involvement on Body Image Perception and Ideals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Concerns about body image may affect athletes, mainly because of specific sports models to achieve successful performance. This systematic review reports on body image dissatisfaction (BID) in athletes following the guidelines for systematic reviews and meta-analyses. From a total of 887 articles identified through a systematic search of electronic databases, 15 articles conducted on 2412 athletes were included in this review. To be eligible for inclusion, the studies must have been published in the last ten years up until September 2022 and analyze body image perception using body figure drawings and anthropometric profiles. The quality of the included studies was evaluated using the adapted Newcastle-Ottawa Scale for observational studies. These studies were compared through thematic analysis of BID to develop four general issues, including gender, sport type and level, and weight status. According to the meta-analyses, the significant medium and small effect sizes found for gender and weight status, respectively, indicated that male athletes had lower BID than females and that, among the latter, normal-weight athletes had a higher BID than underweight ones. The implications and limitations of the included studies are discussed extensively in this review, highlighting the need for further research on BID examined both in the social and the sports contexts. Sports activity should be carried out following healthy lifestyles, and promoting positive BI.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".