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The relationship between core endurance and balance in premenopausal nursing population

2024· article· en· W4403648793 on OpenAlexaboutno aff
Kubal Swati, Mohite Akanksha, Lokwani Mahek

Bibliographic record

VenueInternational Journal of Pharmaceutical and Clinical Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)MedicineCore (optical fiber)PsychologyPhysical medicine and rehabilitationComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.624
GPT teacher head0.732
Teacher spread0.108 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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