*WINNER* What is the Relationship Between Balance and Core Strength?
Bibliographic record
Abstract
Balance is a key factor in sports rehabilitation, training, and performance. It has been suggested that strengthening the core muscles may improve balance. Studies have reported (Ambegaonkar et al., 2014; Kahle & Gribble, 2009) that core strength may be related to balance; however, other studies (Gordon et al., 2013; Ozmen, 2016) have stated that further research is needed to determine the correlation. The purpose of the study was to determine if there was a relationship between balance and core strength. Twenty-one Tennessee Tech Exercise Science students were selected to participate, with ages ranging from 20 to 25 years old; additionally, seven Division 1 athletes were included in our sample. All participants performed a modified version of the Star Excursion Balance Test (SEBT), a functional screening tool to assess balance, in addition to the McGill’s Lateral Musculature Plank Test, which evaluates the strength of the core muscles essential to stabilizing the back. The authors hypothesized that there would be a positive correlation between balance and core strength, and our results justified that by showing an r-value of 0.33, which was positive and weak. The r-value of 0.33 was not significant at alpha = .05. While further research should be conducted with a larger sample size to determine what other factor(s) provide a higher degree of explanatory value, our findings will benefit individuals or sports professionals, such as coaches or rehabilitation therapists, who are looking for more information about how balance relates to core strength.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".