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Record W6899289696 · doi:10.5878/000441

Musculoskeletal disorders and stress in checkout cashiers 2005

2013· dataset· en· W6899289696 on OpenAlexaff

Bibliographic record

VenueSwedish National Data Service · 2013
Typedataset
Languageen
Field
Topic
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsPaymentWork (physics)Physical stressStress (linguistics)Work stressMusculoskeletal disorderPsychological stress

Abstract

fetched live from OpenAlex

As a complement to an investigation of work movements, the prevalence of musculoskeletal disorders and stress at work was investigated among checkout cashiers in a number of shops belonging to the same company during May 2005. The disorder prevalence was assessed by a NMR-questionnaire form. The stress load was assessed by a stress-energy questionnaire according to Kjellberg and Iwanowski. In addition, questions about stressing factors at work were put. Fifty cashiers from seven shops in a metropolitan area filled in the questionnaire. The cashiers had an average age of 24 years. In spite of this the prevalence of musculoskeletal disorders was quite high. The stress-energy results showed a picture of experienced stress in many subjects but positive energy readings in most subjects. Several operations were experienced as physically straining, especially lifting heavy objects. The behaviour of the customers was the major course for stress. Unfriendly or tardy behaviour, piling up products, complaints about and return of products were mentioned. Bad design or function of the computer system or other technical equipment were other sources of stress. Regarding the interaction with the customers the payment situation was an activity causing irritation. Purpose: To study physical and psychological burden on the cashiers in grocery stores.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.301
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.007

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 head0.028
GPT teacher head0.310
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2013
Admission routes1
Has abstractyes

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