MétaCan
Menu
Back to cohort
Record W2987151599 · doi:10.1080/24725838.2019.1688894

Ranking Stretcher and Backboard Related Paramedic Lifting Tasks Based on Their Biomechanical Demand on the Low Back

2019· article· en· W2987151599 on OpenAlexaff
Daniel P. Armstrong, Paul J. Makhoul, Kathryn E. Sinden, Steven L. Fischer

Bibliographic record

VenueIISE Transactions on Occupational Ergonomics and Human Factors · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsLakehead UniversityQueen's UniversityUniversity of Waterloo
Fundersnot available
KeywordsSCOOPWork (physics)Back injuryTask (project management)Compression (physics)Sagittal planeStructural engineeringComputer sciencePhysical medicine and rehabilitationMedicinePhysical therapyEngineeringMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

Occupational ApplicationsWe rank ordered essential paramedic lifting tasks by biomechanical exposure measures at the low back, to understand aspects of paramedic work that impose the greatest risk of musculoskeletal disorders. Specifically, stretcher raising, lowering, and loading, and scoop stretcher lifting, were ordered in terms of peak sagittal low back angles, peak low back compression and antero-posterior shear forces, estimated cumulative damage over a work shift, and the probability of being a high-risk work task. We found that scoop stretcher lifting imposed the highest peak low back angles and both compression and shear forces, while stretcher raising had the greatest estimated cumulative damage and probability of being a high-risk task. All tasks resulted in peak low back compression forces that exceeded injury risk guidelines, and all tasks were identified as high-risk based on estimated cumulative damage. These results highlight the need for interventions in the paramedic sector to reduce the resultant biomechanical exposures, prioritizing efforts towards scoop board lifting and stretcher raising.

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 score0.465
Threshold uncertainty score0.915

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)0.0010.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIISE Transactions on Occupational Ergonomics and Human FactorsSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207