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Record W4401009328 · doi:10.1016/j.apergo.2024.104361

A mixed methods approach to describe the efficacy of lift assist device use to reduce low back musculoskeletal disorder risk factors during three common patient extrication scenarios

2024· article· en· W4401009328 on OpenAlexaff
Kate M. Posluszny, Daphne C. Ho, Sanjay Veerasammy, Cindi Taylor, R. Stuart McDougall, Steven L. Fischer

Bibliographic record

VenueApplied Ergonomics · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsRegional Municipality of NiagaraUniversity of Waterloo
Fundersnot available
KeywordsLift (data mining)Perceived exertionPhysical medicine and rehabilitationExertionBack injuryPhysical therapyMedicineSimulationComputer scienceHeart rate

Abstract

fetched live from OpenAlex

This mixed-method study evaluated the efficacy of lift assist device use (Binder®, Eagle®, Maxi Air®) relative to manual lifting/care-as-usual in reducing low back muscle activity and perceived exertion during simulated patient extrication tasks. User feedback was recorded to identify factors that might influence use. Twenty paramedics performed a floor to stretcher lift, lateral transfer, and confined space extrication care-as-usual and with lift assist devices. Use of a lift assist reduced low back muscle activity during floor to stretcher and confined space tasks by 34-47%. Paramedics perceived exertion decreased from 'somewhat hard' to 'light' or 'very light' when using an assistive device. Paramedics noted that ease of use, patient comfort, task time, patient acuity, among other considerations would influence use decisions. Lift assist devices were efficacious at reducing low back muscle activity and perceived exertion during floor to stretcher and patient extrication tasks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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