MétaCan
Menu
Back to cohort
Record W1505065596 · doi:10.1177/154193120605000703

Office Ergonomics Intervention Study Panel

2006· article· en· W1505065596 on OpenAlexaff
Benjamin C. Amick, Michelle M. Robertson, Lianna Bazzani, Cammie Chaumont Menéndez, Kelly DeRango, Anne Moore

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2006
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsYork University
FundersNational Institute for Occupational Safety and HealthLiberty Mutual Research Institute for SafetySteelcase
KeywordsHuman factors and ergonomicsPsychological interventionProductivityMultidisciplinary approachIntervention (counseling)EngineeringApplied psychologyPsychologyMedical educationPoison controlMedicineNursingEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Ergonomic issues in the office environment affect both workplace health and productivity outcomes. Currently the office ergonomics intervention literature is underrepresented in research studies with rigorous study designs and analytical methods. Furthermore, there exist no published replication intervention studies. Panel members are part of a multidisciplinary inter-institutional research group that conducted the same office ergonomics intervention study at two different worksites (one public and one private sector). Office workers agreeing to participate were assigned to one of two interventions - a highly adjustable chair coupled with an office ergonomics training (chair-with-training group) or the office ergonomics training alone (training-only group) - or a control group receiving the training at the end. During this discussion panel the effects of the interventions on ergonomics knowledge and computing behaviors, biomechanical changes, individual components of musculoskeletal and visual symptoms and productivity will be presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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.

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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207