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Record W3026217800 · doi:10.1016/j.apergo.2020.103144

Exploring the need for and application of human factors and ergonomics in ambulance design: Overcoming the barriers with technical standards

2020· article· en· W3026217800 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Ergonomics · 2020
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversity of WaterlooConestoga College
FundersDefence Research and Development Canada
KeywordsHuman factors and ergonomicsEngineeringPoison controlAeronauticsMedical emergencyMedicine

Abstract

fetched live from OpenAlex

Ergonomic risk factors, such as excessive physical effort, awkward postures or repetitive movements, were the leading causes of injuries amongst EMS workers in the United States, of which 90% were attributed to lifting, carrying, or transferring a patient and/or equipment. Although the essential tasks of patient handling, transport, and care cannot be eliminated, the design of ambulances and associated equipment is modifiable. Our aims were to identify the extent of Human Factors and Ergonomic (HFE) considerations in existing ambulance design standards/regulations, and describe how HFE and the standards/regulations were applied in the EMS system. Through an extensive environmental scan of jurisdictionally relevant standards/regulations and key informant interviews, our findings demonstrated that existing standards/regulations had limited considerations for HFE. As a result, HFE principles continue to be considered reactively through retrofit rather than proactively in upstream design. We recommend that performance-based HFE requirements be integrated directly into ambulance design standards.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.534

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)0.0000.000

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 head0.035
GPT teacher head0.224
Teacher spread0.189 · 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