Comparing the Effects of Core Stability and Williams Training on Dynamic Balance and Back Pain in Women With Chronic Back Pai
Bibliographic record
Abstract
Introduction: This research aimed to compare the effects of the Williams and core stability training on dynamic balance and back pain in women with chronic back pain. Materials and Methods: In total, 45 women with chronic back pain were selected as the available sample and were randomly divided into 3 groups of 15 participants, including core stability, Williams, and control. Before the beginning and the end of the training period, the dynamic balance with the Star Excursion Balance Test (SEBT) and low back pain with Québec Questionnaire was measured. To analyze the obtained data, Analysis of Covariance (ANCOVA) was used in SPSS at P<0.05. Results: The present study findings revealed a significant difference in core stability and Williams training on dynamic balance and improvement in the extent of low back pain in the study participants. There was a significant difference between the training groups in dynamic balance; however, there was no significant difference in the improvement of low back pain between the experimental groups. Conclusion: To improve dynamic balance, a core stability training program is recommended, and Williams’ flexor movements are more appropriate for reducing low back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".