Making the cut: Investigating body image and well-being among female powerlifters
Bibliographic record
Abstract
In weight-category sports, purposeful weight loss (PWL) is often undertaken preceding a competition to gain a performance advantage at a lower body weight. Researchers investigating PWL among powerlifters have identified associations to psychological outcomes. Yet investigations considering the psychological outcomes of PWL can be expanded to include (1) broader conceptualizations of psychological concepts and (2) greater nuance for the dynamics of PWL. Moving towards addressing these research gaps, the purpose of this study was to examine body image and well-being in female powerlifters during a period of PWL surrounding competition. Using a non-experimental longitudinal design, female powerlifters ( N = 12; Mage = 29.42, SD age = 9.23 years) self-reported body weight, body image, and well-being at five timepoints over 10 weeks. Body image was measured using the Body Appreciation Scale-2 along with a single-item indicator of shape and weight satisfaction. Well-being was measured using the Warwick Edinburgh Mental Well-Being Scale. At the time of official competition weigh-in, participants lost an average of 3.44 kg of body weight ( SD = 1.14 kg). One pooled time series regression analysis was used per response variable (body appreciation/shape satisfaction/weight satisfaction/well-being) to test the temporal association with body weight. Body weight predicted weight satisfaction ( B = 0.40, p < .001) and well-being ( B = –0.19, p < .001). It can be concluded that during a nine-week period of PWL female powerlifters reported improvements in weight satisfaction and well-being. These findings help to understand psychological outcomes for gradual weight loss practices among female powerlifters when preparing to compete.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".