The relationship between core endurance and balance in premenopausal nursing population
Bibliographic record
Abstract
Objectives: To study the relationship between core endurance and balance in the premenopausal nursing population. Materials and Methods: Materials required for this study included Adjustable plinth, Jig, Straps, Adhesive tapes, Stopwatch, Scale, Measuring tape. It was an observational analytical study on 75 primary premenopausal nurses between the age group of 30 – 40 years. We excluded any individual with lower extremity injury, any neurological or musculoskeletal condition affecting balance, class 2 and class 3 obesity, participants who couldn’t attain McGill core endurance test postures and the ones who couldn’t maintain single leg stance for at least 5 seconds. After obtaining a written consent, the participants were assessed for core endurance using The McGill core endurance tests and for balance using Star Excursion Balance Test (Y balance). It took approximately 30 minutes to complete. Data was collected and analyzed for Normality using Kolmogorov- Smirnov normality test. As the data did not pass normality, Spearman’s correlation coefficient test was used to find the relationship between core endurance and balance in premenopausal nursing population. Results: It was observed that there is a positive non-significant relationship between core endurance (Flexor, extensor, dominant and non-dominant side bridge) and balance (Anterior, postero medial, posterolateral CRDS scores) of bilateral lower limbs.
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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 | 0.000 | 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".