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Record W3123800964 · doi:10.5430/ijfr.v12n2p184

Problems of an Ergonomic Approach to the Design of a Uniform Medical Suit

2021· article· en· W3123800964 on OpenAlexvenueno aff
Venera Yumagulova, Elmira G. Akhmetshina, Gulnaz R. Ahmetshina

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsnot available
FundersKazan Federal University
KeywordsDocumentationTechnical documentationProcess (computing)Human factors and ergonomicsObject (grammar)Computer scienceIndustrial designEngineering managementManufacturing engineeringRisk analysis (engineering)Operations managementEngineeringBusinessPoison controlMedicineMechanical engineeringMedical emergencyArtificial intelligence

Abstract

fetched live from OpenAlex

The article presents the research in the design of a uniform medical suit and methods of implementing ergonomic requirements in the process of solving problems. The authors have chosen special uniforms for surgeons as an object of study and analysis. As a result of comparative analysis, it is noted that currently, the assortment does not meet consumer needs since many manufacturers do not attach much importance to the ergonomic requirements. The current domestic standards and regulatory and technical documents also do not always take into account the specialization and working conditions of a specialist. In the course of targeted research, the whole range of issues related to the pre-project analysis of the situation was resolved, the sequence of development of the design solution was indicated. The authors have given specific practical recommendations for the preparation of design documentation for a new model of a women's surgical blouse. The developed design documentation can serve as an information source when launching new models into industrial production since it is designed to meet the requirements for the ergonomics of surgeons.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.077
GPT teacher head0.332
Teacher spread0.255 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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