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Record W4388697381 · doi:10.1080/00140139.2023.2284682

Investigating the influence of keyboard inclinations on sitting and standing workstations

2023· article· en· W4388697381 on OpenAlexafffund
Victor Eghujovbo, Eun‐Sik Kim

Bibliographic record

VenueErgonomics · 2023
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsSittingWorkstationWristForearmCarpal tunnel syndromeCumulative trauma disorderPhysical medicine and rehabilitationWork (physics)Musculoskeletal disorderPhysical therapyComputer usersPosition (finance)SimulationComputer scienceMedicineEngineeringHuman factors and ergonomicsPoison controlSurgeryMultimediaMechanical engineeringMedical emergencyBusiness

Abstract

fetched live from OpenAlex

Given the growing global computer workforce, concerns exist about the escalation of computer related injury with Carpal tunnel syndrome (CTS) being one of the most reported work-related musculoskeletal disorders (WMSDs) among office workers. The optimal range of keyboard angles for sitting and standing positions based on wrist posture, forearm muscle activities and user preference as well as the keyboard location in relation to user position were analysed. 30 volunteers with an above 40 words per minute typing speed participated in this study. Result show that, although user prefer to use positive keyboard angle, the negatively tilted keyboard is more ergonomically friendly at both sitting and standing workstations, reducing muscle activity and awkward wrist posture while maintaining performance. The findings indicate that negative sloped keyboard might have the possibility to reduce the risk of developing CTS in office workers.Practitioner summary: This study determines the range of optimal slope of keyboard angle in a sit and stand workstation. Our results indicate a trend in the negative slope keyboard as an ergonomically friendly option for the intervention to Carpal tunnel pressure.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.027
GPT teacher head0.318
Teacher spread0.292 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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