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Record W4236060124 · doi:10.1002/hfm.20098

Mechanical exposure and musculoskeletal disorder risk at the production system level: A framework and application

2008· article· en· W4236060124 on OpenAlexafffund
Silvia A. Pascual, Mardy B. Frazer, Richard Wells, Donald C. Cole

Bibliographic record

VenueHuman Factors and Ergonomics in Manufacturing & Service Industries · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of TorontoUniversity of Waterloo
FundersUniversity of WaterlooWorkplace Safety and Insurance Board
KeywordsProduction (economics)Production system (computer science)Intervention (counseling)Product (mathematics)Musculoskeletal disorderOperations managementRisk analysis (engineering)MedicineEnvironmental healthEngineeringHuman factors and ergonomicsMathematicsPoison controlNursing

Abstract

fetched live from OpenAlex

Abstract Intervention research to reduce musculoskeletal disorders (MSDs) has usually been focused at the job level. However, to intervene in and monitor change at a production system level, an approach to assess mechanical exposure and risk at this level is needed. The first purpose of this article is to present an example of production system level risk estimates. The second purpose is to compare the impact of an intervention at the job system and the production system level. One product was tracked throughout an entire production system to quantify the “product cycle” exposure and risks experienced by the low back. The intervention described decreased the predicted production system exposures and predicted risk substantially, if adopted across all departments. © 2008 Wiley Periodicals, Inc.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.632

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.255
Teacher spread0.234 · 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

Citations5
Published2008
Admission routes2
Has abstractyes

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