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Record W4403659815 · doi:10.6007/ijarbss/v14-i10/23315

Optimising Posture: An Experimental Study of Posture Support for Comfort among University Students

2024· article· en· W4403659815 on OpenAlexaboutno aff
Ayuni Nabilah Alias, Karmegam Karuppiah, Kaushaalya Sanmugam

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationApplied psychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Introduction: Long periods of sitting contribute to musculoskeletal disorders (MSD), which can lead to occupational injuries.Introducing posture support is one of the strategies targeted at alleviating discomfort caused by prolonged sitting.The purpose of this study is to determine the effectiveness of posture support in improving muscle comfort during prolonged sitting among university students.Methods: An experimental study was conducted with 32 participants, evenly divided into control and experimental groups of 16 each.The experimental group received posture support during the two-hour simulation, but the control group did not.The Short-Form McGill Pain Questionnaire and Visual Analogue Scale were used to assess the subjects' level of discomfort every 15 minutes.Results: The control group's mean discomfort ratings for several body areas, including the neck, shoulders, arms and hands, upper back, lower back, buttocks, thighs, knees, feet, and ankles, were substantially higher than the experimental group's (p<0.05).Conclusion: Posture support effectively alleviates discomfort in students' sitting posture, as indicated by the experimental group's reduced discomfort.To better understand this phenomenon, future research should include larger sample sizes, longer study periods, and real settings in university's lecture hall.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.118
GPT teacher head0.507
Teacher spread0.390 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venueInternational Journal of Academic Research in Business and Social SciencesSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207