Influence of compressive knee wraps on squat self-efficacy
Bibliographic record
Abstract
It is widely known that exercise is a key aspect of maintaining good health, and a key component of exercise is resistance training. A very commonly cited barrier to exercise initiation and adherence is low self-efficacy (SE). One possible way to increase SE may be through the use of supportive weight lifting equipment, such as knee wraps or sleeves. So far, no research has been conducted surrounding the influence of supportive equipment and SE. The purpose of this study was to determine what effect, if any, compressive knee wraps had on squat SE in non-competitive exercisers. Nine non-competitive exercisers (minimum four months of experience) with the ability to properly perform a squat were used as the sample. Measures taken were SE before/after using knee wraps, and desired weight adjustment after using wraps. Participants were instructed to complete their normal warm-up, to 75% of their predicted squat max. Subjects indicated their SE for this weight and performed a single repetition. Subjects applied knee wraps and were asked to repeat the same process with the same weight. Following the wrapped set the participant was asked if they would like to adjust the weight. No significant differences in squat SE with use of the knee wraps were observed. Goal setting (measured by how much weight the participant wished to add following use of the wraps) was unaffected. Knee wraps do not appear to be an effective way of increasing squat SE in a recreational exercise population.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".