Making the cut: An examination of body appreciation and well-being among female powerlifters
Bibliographic record
Abstract
In weight class sports, purposeful weight loss (PWL) is often undertaken preceding a competition to gain an advantage at a lower body weight. Researcher focus on PWL has been centered on (1) predominantly male competitors and (2) associations to physiological and performance outcomes with considerably less understanding of psychological outcomes. Moving towards a better understanding of psychological outcomes among female athletes, 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 years, SDage = 9.23 years) self-reported data across five time points over 10 weeks. Athletes were asked to track weight change using self-reported of body weight. Body image was measured using the Body Appreciation Scale-2 along with a single-item indicator of both shape satisfaction 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). Four separate pooled time series regression analyses were used to test the temporal association between weight loss and body appreciation/shape satisfaction/weight satisfaction/well-being. Body weight was a significant predictor of well-being (B = –0.19, p = < .001) and weight satisfaction (B = 0.40, p < .001). Therefore, it can be concluded that during a period of PWL, well-being and weight satisfaction improved. Current weight loss guidelines for sport include physiological considerations which may be integrated with psychological outcomes to reflect the well-being of weight class athletes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".