Is purposeful weight loss for competition associated with 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 a competitive advantage at a lower body weight. Investigations of PWL have been centered on (1) predominantly male competitors and (2) associations to physiological and performance outcomes with considerably less understanding of psychological outcomes. To fill these research gaps, the purpose of this study was to examine body appreciation and well-being in female powerlifters during a period of PWL for competition. Using a non-experimental longitudinal design, female powerlifters (N = 12; Mage = 29.42, SD = 9.23 years) self-reported data across four time points over nine weeks. The final time point was 24 hours before competition. Changes in weight were tracked through self-reported body weight. Body appreciation and well-being were measured using the Body Appreciation Scale-2 and Warwick Edinburgh Mental Well-being Scale, respectively. Participants lost an average of 3.08 kg of body weight (SD = 0.85 kg). Separate pooled time series regression analyses were used to evaluate the temporal association between body weight with body appreciation/well-being. Body weight was unrelated to body appreciation (B = 0.01, p = .89) and predicted well-being (B = –0.12, p = .02). Based on these findings, female powerlifters maintained appreciation of the body and improved well-being during PWL for competition. Given that current weight loss guidelines for weight class athletes are predominantly informed by physiological considerations, these findings help to understand psychological outcomes that may be used to further inform 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".