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Record W2053170555 · doi:10.1080/00140139.2014.914582

Prevalence of occupation-related pain among baristas and an examination of low back and shoulder demand during the preparation of espresso-based beverages

2014· article· en· W2053170555 on OpenAlexafffund
R. Scott Dainty, Eric Alcorn, Chantelle A. Ferguson, Diane E. Gregory

Bibliographic record

VenueErgonomics · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityWilfrid Laurier University
FundersNational Institute for Occupational Safety and HealthWorkplace Safety and Insurance Board
KeywordsLow back painMedicineOn demandPhysical therapyForensic engineeringEngineeringBusinessAlternative medicineCommercePathology

Abstract

fetched live from OpenAlex

Many baristas complain of low back pain (LBP) and upper extremity discomfort while at work. This study documented the prevalence of LBP and shoulder pain, via questionnaire, among a population of baristas to determine whether cumulative low back loads and shoulder moments are associated with pain reporting. Fifty-nine baristas completed the questionnaire; ten were also video-recorded for biomechanical analysis while making espresso beverages and cumulative and peak low back loads and shoulder moments were calculated. Seventy-three percent of those who completed the questionnaire reported having experienced LBP, and half attributed this pain to their job as a barista. Furthermore, 68% reported having experienced shoulder pain and half also attributed this pain to their job. Those who suffered from LBP had higher peak low back compression and those with shoulder pain had, in general, higher moments about their dominant shoulder.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.216

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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