MétaCan
Menu
Back to cohort
Record W1662681465 · doi:10.3233/wor-2007-00598

Direct observation of computer workplace risk factors of college students

2007· article· en· W1662681465 on OpenAlexaff
Jessica M. Tullar, Benjamin C. Amick, Michelle M. Robertson, Anne H. Fossel, Chris Coley, Nathaniel Hupert, Mark Jenkins, Jeffrey N. Katz

Bibliographic record

VenueWork · 2007
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsInstitute for Work & Health
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsDeskPhysical therapyForearmMedicineWristPsychological interventionPopulationPhysical medicine and rehabilitationEnvironmental healthSurgeryNursingEngineering

Abstract

fetched live from OpenAlex

Recently, researchers have reported high musculoskeletal symptom prevalence at several US colleges. Since ergonomic interventions have been shown to prevent and reduce disability, it is important to identify the risk factors for developing symptoms among college students. A nested case-control study was completed to determine computer-related ergonomic risks associated with musculoskeletal symptoms. A trained observer completed ergonomic assessments on 52 randomly selected cases and controls. More than 75 percent (cases and controls combined) of the population was exposed to nine potential postural strains including: arms not along side during keying or mousing; lower back not supported; not having chair accessories; computer monitor not adjustable; mouse being too high or low; hand/wrist/forearm in contact with the desk edge; lack of wrist support; and keyboard not being adjustable. Cases and controls were equally likely to have substantially elevated risks but because the sample was small and lacked power, no risks were statistically significant. Since many known risk factors were prevalent in cases and controls, more research is required to evaluate and prevent injury in this population.

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 score0.011
Threshold uncertainty score0.324

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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueWorkSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207