MétaCan
Menu
Back to cohort
Record W2335748314 · doi:10.1177/2165079916630552

Do Personal Factors or Types of Physical Tasks Predict Workplace Injury?

2016· article· en· W2335748314 on OpenAlexafffund
Nelson Ositadimma Oranye, Bernadine Wallis, Kim Roer, Gail Archer-Heese, Zaklina Aguilar

Bibliographic record

VenueWorkplace Health & Safety · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsHealth Sciences CentreSt. Boniface HospitalUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsOccupational safety and healthHealth careMusculoskeletal disorderOddsWork-related musculoskeletal disordersMedicineOdds ratioHuman factors and ergonomicsMusculoskeletal injuryPhysical therapyPsychologyPoison controlMedical emergencyLogistic regression

Abstract

fetched live from OpenAlex

Occupational health research has shown that certain worker and job characteristics are risk factors for workplace injuries. Workers who engage in physically demanding jobs, especially those jobs that involve repetitive motion, are at greater risk for work-related musculoskeletal disorders (WMSD). These risks are particularly prevalent in the health care sector. It is often reported that nurses are at higher risk of workplace musculoskeletal injury than other health care workers due to frequent lifting and transfer of patients and the prevalence of workplace violence. However, many analyses of the physical requirements of jobs do not consider the modifying effect of time spent on a physical task and the risk of WMSD. This study compared the risks of WMSD among workers in health care facilities based on the type of physical tasks and amount of time workers spent on such tasks. Workers who worked longer on a physical task reported more WMSD than those who spent less time on the same physical task. The risk of WMSD was twice as high (odds ratio [OR] = 2.3) among workers who sit less than 2 hours each day compared with those who sit longer. This study found that physical tasks associated with health care jobs and the amount of time spent on these tasks constitutes serious risk factors for WMSD.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.061
GPT teacher head0.446
Teacher spread0.385 · 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 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

Citations21
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueWorkplace Health & SafetySame topicOccupational Health and Safety ResearchFrench-language works237,207