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Record W4402832647 · doi:10.1016/j.apergo.2024.104393

From unknown to familiar: An exploratory longitudinal field study on occupational exoskeletons adoption

2024· article· en· W4402832647 on OpenAlexfundno aff
Julien Cegarra, Jean-Jacques Atain Kouadio, Liên Wioland

Bibliographic record

VenueApplied Ergonomics · 2024
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
FundersInstitut National de Recherche et de SécuritéInstitut national de la recherche scientifique
KeywordsExoskeletonField (mathematics)Exploratory researchPsychologyOccupational safety and healthHuman factors and ergonomicsEngineeringApplied psychologyGerontologyPhysical medicine and rehabilitationPoison controlMedicineSociologyEnvironmental healthMathematicsSocial science

Abstract

fetched live from OpenAlex

Occupational exoskeletons hold promise in preventing musculoskeletal disorders, but their effectiveness relies on their long-term use by workers. This study aims to characterize the adoption process of occupational exoskeletons by analyzing the experiences of 25 operators. Using a mixed-methods approach, both quantitative and qualitative data were collected before and during a four-week familiarization period. We primarily focused on users' expectations, subjective assessments over time, and initial experiences. Findings elucidate shifts in operators' perceptions of the devices over time. Through their narratives, we highlight how exoskeleton use impact operators' movements and the subsequent adaptations. Operators demonstrated diverse exploratory behaviors, indicating their efforts to get to grips with the effects of exoskeletons in their own ways. This study offers insights into the initial stages of occupational exoskeleton adoption, thus enriching our comprehension of rejection patterns and pathways toward their widespread acceptance.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.671

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 head0.022
GPT teacher head0.266
Teacher spread0.244 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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