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Record W2333473782 · doi:10.1177/154193120204601306

Methodological Approaches to Research on Musculoskeletal Complaints and Injuries

2002· article· en· W2333473782 on OpenAlexaff
Krystyna Gielo‐Perczak, Waldemar Karwowski, Shrawan Kumar, William S. Marras

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2002
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCausality (physics)Risk analysis (engineering)Fuzzy logicWork (physics)Human factors and ergonomicsComputer scienceJoint (building)Applied psychologyManagement sciencePoison controlMedicinePsychologyEngineeringArtificial intelligenceMedical emergencyMechanical engineering

Abstract

fetched live from OpenAlex

An important aspect of workplace design is the creation of possible ways to bring innovations to the prevention of excessive joint loading. The solutions for finding these different options are new theoretical concepts with applications of biomechanics and fuzzy logic, innovative insights into the human body using simulation tools, critical examinations of the relationship between workplace analysis and causality in the control of musculoskeletal disorders, and inventive studies of their validity through epidemiology. These methodological approaches can be useful tools for minimizing incompatibilities between the capabilities of workers and the demands of their jobs and prevention of likely musculoskeletal injuries during work. The results of these approaches can assess the suitability of the designed human-machine system and determine possible improvements in the workplace.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.212
metaresearch head score (Gemma)0.312
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2120.312
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0190.016
Science and technology studies0.0040.020
Scholarly communication0.0080.006
Open science0.0050.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.285
GPT teacher head0.372
Teacher spread0.087 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207