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Record W3214327598 · doi:10.1177/1071181321651256

Comparison of ergonomic risk factors and work-related musculoskeletal disorders among dismantler and burners of electronic waste in Agbogbloshie, Accra Ghana

2021· article· en· W3214327598 on OpenAlexaff
Augustine A. Acquah, Clive D’Souza, Bernard J. Martin, John Arko‐Mensah, Niladri Basu, Isabella A. Quakyi, Thomas G. Robins, Julius N. Fobil

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsMcGill University
FundersNational Institute for Occupational Safety and HealthFogarty International Center
KeywordsWork-related musculoskeletal disordersWork (physics)Electronic wasteHuman factors and ergonomicsMedicineMusculoskeletal disorderEnvironmental healthEngineeringWaste managementMechanical engineeringPoison control

Abstract

fetched live from OpenAlex

Rudimentary methods for electronic waste (e-waste) recycling employed in developing countries are a source of work-related musculoskeletal disorders (WRMSDs). A summarized comparison of WRMSDs and preliminary exposure assessment among e-waste dismantlers (D) and burners (B) in Agbogbloshie, Ghana is presented. A cross-sectional study was conducted to investigate WRMSDs and associated risk factors using the Cornell Musculoskeletal Discomfort Questionnaire and a newly developed ergonomic assessment tool. Results indicated higher WRMSDs prevalence in the lower back (68% D vs. 52% B; p = 0.172), shoulder (41% D vs. 29% B; p = 0.279) and upper arm (33% D vs 5% B; p = 0.010). Moderate to severe trunk flexion, high force exertion, repetition and vibration were prevalent risk factors among workers and were significantly higher in dismantlers than burners ( p ≤ 0.001). Detailed ergonomic studies investigating the relationship between physical exposures and WRMSDs are needed to provide a deeper understanding of WRMSD causation in e-waste workers and more particularly in unstructured, unregulated work.

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 score0.031
Threshold uncertainty score0.733

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicRecycling and Waste Management TechniquesFrench-language works237,207